Evaluation of post implantation patient status and medical device performance

ABSTRACT

Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises providing an interactive session configured to allow a user to navigate a plurality of sub sessions, determining a first set of data items in accordance with a first subsession, the first set of data items including the image data, determining a second set of data items in accordance with a second subsession of the interactive session, determining, based at least in part on the first set of data items and the second set of data items, an abnormality, and outputting a post-implant report of the interactive session.

This application claims the benefit of U.S. Provisional PatentApplication No. 62/844,557, filed May 7, 2019, the entirety of which ishereby incorporated by reference.

FIELD

This disclosure relates to medical devices, and in some specificexamples, relates to computing devices (e.g., mobile devices) configuredto evaluate a patient's recovery from implantation of a medical deviceand/or to evaluate the performance of a medical device that is presentlyimplanted in the patient.

BACKGROUND

Medical devices may be used to treat a variety of medical conditions.Example medical devices include implantable medical devices (IMDs), suchas cardiac or cardiovascular implantable electronic devices (CIED). AnIMD, also referred to at times as an “implanted medical device,” mayinclude a device implanted in a patient at a surgically or procedurallyprepared implantation site. The IMD may include a diagnostic deviceconfigured to diagnose various ailments of a patient, monitor a healthstatus of the patient, etc. In addition, or alternatively, an IMD mayalso be configured to deliver electrical stimulation therapy to apatient via electrodes, such as implanted electrodes, where the devicemay be configured to stimulate the heart, nerves, muscles, brain tissue,etc. In any case, the IMD may, in some instances, include a batterypowered component, such as when referring to implantable cardiacpacemakers, implantable cardioverter-defibrillators (ICDs), otherelectrical stimulators including spinal cord, deep brain, nerve, andmuscle stimulators, infusion devices, cardiac and other physiologicmonitors, cochlear implants, etc. In such instances, the battery poweredcomponent of the IMD may be implanted, such as at a surgically orprocedurally prepared implantation site. In addition, associateddevices, such as elongated medical electrical leads or drug deliverycatheters, can extend from the IMD to other subcutaneous implantationsites or in some instances, deeper into the body, such as to organs orvarious other implantation sites.

While preparation and implantation are conducted in a sterile field, andthe IMD components are packaged in sterile containers or sterilizedprior to introduction into the sterile field, there can still be a riskof introduction of microbes into the sites. Implanting clinicianstherefore typically apply disinfectant or antiseptic agents to the skinat the surgical site prior to surgery, directly to the site before theincision is closed, and prescribe oral antibiotics for the patient toingest during recovery. Despite these precautions, infections can occur.

Accordingly, infection associated with implantation of medical devicesremains a health and economic concern. If an infection associated withan IMD occurs, explanting the device can often be the only appropriatecourse of action. In addition, once a site becomes infected, theinfection can migrate, e.g., along the lead or catheter to locations inwhich the lead and/or catheter are implanted. Removal of a chronicallyimplanted lead or catheter, e.g., in response to such an infection, canbe difficult. Aggressive systemic drug treatment is prescribed to treatsuch infections. However, early detection of infections associated withIMDs may allow for earlier intervention, resulting in fewer deviceexplants.

In some instances, patients who have had certain medical devicesimplanted, such as CIEDs, are required to have a post-implant follow-upconsultation visit with a healthcare professional (HCP). The HCP visittypically occurs anywhere from a few days to a few weeks after theimplantation. The HCP typically performs a wound check and possiblyinitiates a device interrogation session to determine performancemetrics of the CIED These visits are non-remarkable and brief in themajority of cases, at a level as high as over 90%. However, thepost-implant follow-up visit may still constitute for the patient arequired checkpoint with the HCP. In some cases, this follow-up visituncovers a potential infection or device-related complications thatpotentially warrant clinical intervention.

SUMMARY

While the actual follow-up consultation visits with the healthcareprofessional (HCP) tend to be relatively short, the visits can stillconstitute a burden on the patient's life, the physician's clinic, andon the healthcare system overall. Aspects of this disclosure aredirected to one or more computing devices having processing circuitry,where the processing circuitry is configured to facilitate or simulate avirtual check-in for a patient, such as a virtual follow-up or otherwellness check, where a patient and/or physician may check on the statusof medical devices, including implantable medical devices (IMDs), and/oron the patients themselves.

A system of processing circuitry of the one or more computing devicesmay implement various tools and techniques (hereinafter, “tools,” asystem of processors, or a processing circuitry system) in order toprovide a virtual check-in for the patient. The processing circuitrysystem may be configured to provide an option for the patient to engagein an interactive session (e.g., virtual check-in process, interactivecheck-in session, interactive reporting process, etc.) as part of apost-implant evaluation, for which the patient can undergo aninteractive reporting session remotely, as in remotely from a formaldoctor's office setting or other HCP setting. In some examples,processing circuitry of a mobile or portable computing device may beconfigured to manage the interactive session, such that a user may beable to effectively navigate the interactive session from any remotelocation. In addition, the processing circuitry system may communicatepatient and/or medical device data between various computing devices(e.g., end-user mobile devices, camera devices, wearable devices, edgedevices, etc.). In some examples, the processing circuitry system may beconfigured to manage the interactive session to ensure properinteraction with the check-in tools, such that the system may accuratelydetermine whether any complications have arisen with the patient and/orthe 1 MB. The processing circuitry system may communicate suchinformation over a network and/or through implementation of variouscommunication protocols.

In some instances, the processing circuitry system may implement theinteractive session in a secure setting, such as on an authenticatedmobile computing device and/or over a secure data network. In someinstances, the processing circuitry system may implement the interactivesession on a mobile device configured to perform one or more of thevarious techniques of this disclosure, with or without the assistance ofother devices (e.g., network devices). That is, the mobile device mayoperate various software tools configured to perform the virtualcheck-in process. A patient may then conduct an entire session of thevirtual interactive check-in session (e.g., a plurality of subsessionsof an interactive session) without network access or with limitednetwork use as in the use of wireless communication with an edge device(e.g., an IoT device with additional processing resources to assist orperform the various check-in functions). The mobile device may, at alater point in time following the completion of a check-in session(e.g., a plurality of subsessions of an interactive session),synchronize the virtual check-in data with other network devices. Inthis way, a HCP may access the data at that time, where it might not becritical that the HCP access such data in real-time or near real-time asthe patient conducts the particular check-in session (e.g., a pluralityof subsessions of an interactive session).

In accordance with techniques of this disclosure, a processing circuitrysystem is configured to provide a comprehensive user interface (UI) to auser (e.g., the patient), where the UI is configured to guide the userthrough the interactive session and/or an evaluation process. Theprocessing circuitry of a computing device may provide the UI to theuser by generating UI program data and/or accessing UI program data frommemory. In some instances, the processing circuitry may access UIprogram data from a separate computing device of a virtual check-incomputing system. In some instances, the processing circuitry of thecomputing device may tailor the UI program data to each particular useror class of users. In some instances, the processing circuitry systemmay tailor UI program data by deploying various artificial intelligence(AI) algorithms and/or machine learning (ML) models that are configuredto determine a UI that reflects the particular user or class of usersand the one or more medical devices corresponding to the particular useror class of users. In any case, the processing circuitry system may beconfigured to provide, via a display device, the UI to the user of auser-operated computing device.

In some examples, the UI may include interactive UI check-in elements(e.g., UI tiles) configured to guide a patient systematically throughthe virtual check-in process. The UI check-in elements may be configuredto actively assist a particular patient with providing specific patientinput to the system. The patient input may include specific types ofinformation and particular amounts of information. In some examples, theprocessing circuitry system may use the information to, for example,identify various maladies of the patient, such as device pocketinfections, or otherwise, identify abnormalities with the medical deviceand/or the patient. In some examples, the processing circuitry systemmay deploy AI and/or ML models in order to evaluate input received viathe UI in order to determine a health status of the patient and/or astatus of the one or more medical devices, including of an IMD. Inanother example, the processing circuitry system may communicate inputfrom the patient to a HCP. The processing circuitry system may, in turn,receive input from the HCP. In such instances, the processing circuitrysystem may determine, based on the HCP input, the health status of thepatient and/or the status of the one or more medical devices. In anycase, the processing circuitry system may train the AI and/or ML modelson patient input and/or on HCP input, where the processing circuitrysystem may obtain HCP input that, in some instances is based on thepatient input (e.g., uploaded images of implantation site, ECGwaveforms, etc.). Accordingly, the processing circuitry system mayutilize information from multiple sources to determine various maladiesof the patient. While described with reference to a comprehensive UI,the techniques of this disclosure are not so limited, and it will beunderstood that the UI elements may be implemented as stand-alone UIsthat involve a subset of the UI elements of this disclosure. That is,processing circuitry of the computing device may not include all UIelements in a single UI program, and may in some instances, includeadditional UI elements configured to provide various other check-infunctionalities.

In some examples, the UI check-in elements may include generalpatient-status check-in elements, physiological parameter check-inelements, medical device check-in elements, site check elements, such aswound check elements, etc. The processing circuitry system may obtaininformation relating to the various discrete check-in elements anddetermine a health status of the patient, such as a status of an IMD ofthe patient. In some instances, a patient may perform such virtualchecks periodically to check on a status of the medical device orimplantation site. That is, the patient may perform wellness checks evenafter performing a number of virtual check-in sessions (e.g., aplurality of subsessions of an interactive check-in session) that wereeither mandated or recommended by a HCP following a surgical event ofthe patient.

The interactive reporting session can replace, and/or in some instancessupplement, in-person HCP visits. In some examples, the processingcircuitry system of this disclosure may use the results of the computingdevice-implemented check to determine whether or not to provide anindication to the patient, and in certain cases, a physician or otherHCP, as to whether an in-person visit is warranted, or alternatively, ifthe IMD wound recovery is progressing with the patient as expected. Inaddition, a processing circuitry system may implement one or moresubsessions of the interactive session to determine if the one or moremedical devices are functioning within acceptable bounds, etc. In someinstances, the processing circuitry system may obtain information from auser, via one computing device, such as a mobile telephone device, andevaluate the information, via another computing device, such as an edgedevice or a network server. In such instances, an edge device may deployAI and/or ML models trained on medical device data, patient data, and/orheuristic data obtained from a network. The processing circuitry systemmay, in some instances, train the AI or ML models on data obtained fromthe computing device of the user and/or from data obtained directly fromthe medical devices of the patient. In such instances, those medicaldevices may be configured to communicate with an edge device, as well asa user computing device.

In one example, the disclosure provides a method of monitoring a patientof an IMD, the method comprising: providing, via a computing device, aninteractive session configured to allow a user to navigate a pluralityof subsessions comprising at least a first subsession and a secondsubsession distinct from the first subsession, wherein the firstsubsession comprises capturing image data via one or more cameras;determining, via the computing device, a first set of data items inaccordance with the first subsession of the interactive session, thefirst set of data items including the image data; determining, via thecomputing device, a second set of data items in accordance with thesecond subsession of the interactive session, the second set of dataitems distinct from the first set of data items and comprising one ormore of: data obtained from the IMD, at least one physiologicalparameter of the patient, or user-input data; determining, based atleast in part on the first set of data items and the second set of dataitems, an abnormality corresponding to at least one of the patient orthe IMD; and outputting, via the computing device, a post-implant reportof the interactive session, wherein the post-implant report includesindication of the abnormality and indication of an amount of time thathas transpired since a date of implantation of the IMD.

In another example, the disclosure provides a system for monitoring apatient of an implantable medical device (IMD), the system comprising: amemory configured to store image data; and one or more processors incommunication with the memory, the one or more processors configured to:provide an interactive session configured to allow a user to navigate aplurality of subsessions comprising at least a first subsession and asecond subsession distinct from the first subsession, wherein the firstsubsession comprises capturing the image data via one or more cameras;determine a first set of data items in accordance with the firstsubsession of the interactive session, the first set of data itemsincluding the image data; determine a second set of data items inaccordance with the second subsession of the interactive session, thesecond set of data items distinct from the first set of data items andcomprising one or more of: data obtained from the IMD, at least onephysiological parameter of the patient, or user-input data; determine,based at least in part on the first set of data items and the second setof data items, an abnormality corresponding to at least one of thepatient or the IMD; and output a post-implant report of the interactivesession, wherein the post-implant report includes indication of theabnormality and indication of an amount of time that has transpiredsince a date of implantation of the IMD.

The disclosure also provides non-transitory computer-readable mediacomprising instructions that cause a programmable processor to performany of the techniques described herein. In an example, this disclosureprovides a non-transitory computer-readable storage medium having storedthereon instructions that, when executed, cause one or more processorsto: provide an interactive session to a user, the interactive sessionconfigured to allow the user to navigate a plurality of subsessionscomprising at least a first subsession and a second subsession distinctfrom the first subsession, wherein the first subsession comprisescapturing image data via one or more cameras; determine a first set ofdata items in accordance with the first subsession of the interactivesession, the first set of data items including the image data; determinea second set of data items in accordance with the second subsession ofthe interactive session, the second set of data items distinct from thefirst set of data items and comprising one or more of: data obtainedfrom an IMD, at least one physiological parameter of a patient, oruser-input data; determine, based at least in part on the first set ofdata items and the second set of data items, an abnormalitycorresponding to at least one of the patient or the IMD; and output apost-implant report of the interactive session, wherein the post-implantreport includes indication of the abnormality.

The disclosure also provides means for performing any of the techniquesdescribed herein.

The summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the systems, device, and methods describedin detail within the accompanying drawings and description below.Further details of one or more examples of this disclosure are set forthin the accompanying drawings and in the description below. Otherfeatures, objects, and advantages will be apparent from the descriptionand drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates the environment of an example monitoring system inconjunction with a patient, in accordance with one or more techniquesdisclosed herein.

FIG. 2 is a functional block diagram illustrating an exampleconfiguration of an example computing device of FIG. 1, in accordancewith one or more techniques disclosed herein.

FIG. 3 is a block diagram illustrating an example network system thatincludes example computing device(s) of FIG. 1 or 2, a network, edgedevice(s), and server(s), in accordance with one or more techniquesdisclosed herein.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of a medical device of FIGS. 1 and/or 3, in accordancewith one or more techniques disclosed herein.

FIG. 5 is an example user interface (UI) visualization of an examplecomputing device of FIG. 1, 2, or 3, in accordance with one or moretechniques of this disclosure.

FIG. 6 is an UI visualization of an example launch-interactive-sessioninterface, in accordance with one or more techniques of this disclosure.

FIG. 7 is an UI visualization of an example menu interface, inaccordance with one or more techniques of this disclosure.

FIG. 8 is a flowchart illustrating an example method of utilizing UIinput and data processing, in accordance with one or more techniques ofthis disclosure.

FIG. 9 is an UI visualization of an example patient status interfacethat presents upon user request of patient status interface illustratedin FIG. 7, in accordance with one or more techniques of this disclosure.

FIG. 10 is a UI visualization of an example patient status interface, inaccordance with one or more techniques of this disclosure.

FIG. 11 is a UI visualization of an example physiological parametercheck interface, in accordance with one or more techniques of thisdisclosure.

FIG. 12 is a UI visualization of an example physiological parametercheck interface, in accordance with one or more techniques of thisdisclosure.

FIG. 13 is a UI visualization of an example device check interface, inaccordance with one or more techniques of this disclosure.

FIG. 14 is a UI visualization of an example device check interface, inaccordance with one or more techniques of this disclosure.

FIG. 15 is a UI visualization of an example site check interface, inaccordance with one or more techniques of this disclosure.

FIG. 16 is a flowchart illustrating an example method of utilizingimaging techniques, in accordance with one or more techniques of thisdisclosure.

FIG. 17 is a UI visualization of an example site check interface, inaccordance with one or more techniques of this disclosure.

FIG. 18 is a UI visualization of an example site check interface, inaccordance with one or more techniques of this disclosure.

FIG. 19 is a flowchart illustrating an example method of capturing animage of a body over time, in accordance with one or more techniques ofthis disclosure.

FIG. 20 is a flowchart illustrating an example method of navigating aset of UI interfaces of a virtual check-in process, in accordance withone or more techniques of this disclosure.

FIG. 21 is a UI visualization of an example complete check-in interface,in accordance with one or more techniques of this disclosure.

FIG. 22 is a UI visualization of an example complete check-in interface,in accordance with one or more techniques of this disclosure.

FIG. 23 is a flowchart illustrating an example method of determininginstructions for medical intervention concerning an IMD patient, inaccordance with one or more techniques of this disclosure.

Like reference characters denote like elements throughout thedescription and figures.

DETAILED DESCRIPTION

In 2019, over 1.5 million implantable medical device (IMD) implants wereexpected to be implanted in patients worldwide. Per Heart Rhythm Society(HRS) and European Heart Rhythm Association (EHRA) guidelines, each ofthose implants should have had an in-person follow-up within two totwelve weeks. Patient adherence with the post-implant visit improvesmortality and patient outcome. Patient compliance with this visit,however, ranges from 55-80%. For 93-99% of these visits, patientspresent without abnormalities (e.g., infections). That is, many of thesevisits could be performed virtually.

This disclosure presents systems and methods for remote post-IMDmonitoring for implantation site infections. Implantation infections areestimated to occur in about 0.5% of IMD implants and about 2% of IMDreplacements. Early diagnosis of IMD infections can help drive effectiveantibiotic therapy or device removals to treat the infection. This isdone in an in-hospital, post-op setting where the patients can becontinuously monitored.

A post-discharge infection diagnosis typically follows when patientsfeel any infection-related symptoms, such as pain or fever. However, forthe subset of asymptomatic patients with infection that are unable toself-report any implantation site abnormalities, there is a need forexpert review. Since having all patients come-in for a physicalinfection review can be cumbersome for both patients and caregivers,there is a need for remote monitoring of implantation sites.

In general, this disclosure is directed to a browser interface or amobile device app that can be operated via a variety of user-facingcomputing devices, including, but not limited to, a smartphone, tabletcomputer, mobile device, virtual reality (VR), augmented reality (AR),or mixed reality (MR) headset, etc. The computing device may execute asoftware app that causes the computing device to perform variousfunctionalities described herein either locally, using the computingresources of the computing device, or via cloud computing, such as bytransmitting captured data via a network interface to a backend system(e.g., a server system) that performs some or all of the analysisdescribed herein. In addition, as described herein, some or all of theanalysis may be performed, via edge computing, such as by transmittingcaptured data to an edge device (e.g., an IoT device or anothercomputing device). In some examples, edge devices may includeuser-facing or client devices, such as smartphones, tablet computers,PDAs, and other mobile computing devices. In any case, a backend systemmay or may not include certain edge devices as part of the backendsystem. In one examples, a network may interface with one or more edgedevices operating at the edge of the backend system, so as to form anintermediary between a computing device of a user and the variousservers of a network. In such examples, a computing device may performthe various techniques of this disclosure by leveraging edge computing,cloud computing, or a combination thereof. An example combination ofedge and cloud computing may include distributive computing ordistributed computing systems. In any case, the computing device mayperform the techniques of this disclosure in-app, in-tablet and/or incloud environments.

In the cloud-based implementations of this disclosure, the mobile deviceapp may receive various types of post-analysis data from the backendsystem, and present the data to the user, either in as-received form, orafter performing some additional processing or formatting of the datalocally.

The computing device executing the app (e.g., a virtual check-inprocess) may perform various functionalities described below, whethervia local computing resources provided by the computing device, viacloud-based backend systems, or both. In some examples, the computingdevice may implement the app via a web browser. In some examples, thecomputing device may perform a device check. In such examples, thecomputing device may implement one or more interrogations of one or moremedical devices (e.g., IMDs, CIEDs, etc.). In addition, the computingdevice may analyze medical device settings, parameters, and performancemetrics.

In some examples, the computing device may perform a physiologic check.In such examples, the computing device may enable monitoring and/oranalysis of physiologic signals detected from the patient includingelectrocardiogram (ECG) signals, and other health signals that can berecorded (e.g. respiration, impedance, activity, pressure, etc.).

In addition, the computing device may perform a check of one or moresurgical sites (e.g., an implant wound, an implant-removal implantationsite, etc.). In some examples, the computing device may implement imageprocessing with respect to an area indicative of an implantation site(e.g., a wound site). In some examples, the computing device may performimage processing using camera(s) of, or otherwise communicativelycoupled to, the computing device to detect abnormalities, such as inwound healing and/or by determining potential infections at animplantation site.

In some examples, the computing device may perform a patient statuscheck. The app may implement an interactive logging, journaling, ordiary function for the patient to respond to a few key questionsregarding patient health information, medications, symptoms,physiological or anatomical metrics, or any relevant patient-reportedinformation.

The tools of this disclosure may, in some examples, use artificialintelligence (AI) engines and/or machine learning (ML) models. In someexamples, the AI engines may use cohort data to conduct individualchecks. A cohort may include any number of cohorts, including a CIEDcohort that includes CIED patients, an age cohort, a skin pigmentationcohort, an IMD type cohort, etc. or combinations thereof. In addition,the ML models may be trained using cohort data to conduct individualchecks. In the wound check example, the tools of this disclosure mayinvoke image recognition or image processing AI that leverages (e.g., istrained using) wound and infection libraries available from varioussources.

To perform ECG checks, the tools of this disclosure may use arrhythmiaclassification AI with a QRS model to classify normal rhythm and anyunderlying arrhythmias relative to the patient demographics andcharacteristics. It will be understood that a QRS model generally refersto a QRS complex that contains a combination of various graphicaldeflections included with a typical electrocardiogram (e.g., a Q wave,an R wave, an S wave, etc.).

To perform device check functionalities of this disclosure, the app mayleverage various communication hardware components of the computingdevice to interrogate the one or more medical devices (e.g., an IMD) toretrieve medical device parameters specific to the medical devicefamily. The processing circuitry system may be configured to compare theretrieved parameters to the physician's earlier-provided settings aswell as to comparable medical device families for deviation ofnormality/normalcy analysis.

The tools of this disclosure may also provide patient status checkfunctionalities using an interactive session with the patient in whichthe patient provides elicited input to answer questions related to thepatient's health and present condition. In various examples, the toolsof this disclosure may enable the patient to enter information (e.g.,condition information or results) via text entry, dropdown menuselection from prepopulated responses, and/or radio buttons as shown inone or more of the accompanying drawings. In some non-limiting examples,the tools of this disclosure may output questions, to elicit patientresponses by which the patient could enter information such asmedications, doses thereof, as well as other prompted information.

At the completion of the session (e.g., an interactive reportingsession), the tools of this disclosure may mark the results on the UIprovided via the mobile device app, with date and time stamping. Thetools of this disclosure may provide the patient with the capability togenerate a report (e.g., as a portable document format (PDF) or invarious other formats) for the records of the patient, to email orotherwise transfer the report or select contents of the report to familymembers or to a doctor (e.g., via a File Transfer Protocol (FTP)), etc.

The app of this disclosure may, in some non-limiting examples, providethe patient the capability to save the results locally to the computingdevice (e.g., a smartphone or tablet computer) for future comparison,reference, or use as a standalone file. In some instances, the computingdevice may store the results locally to the computing device for use asa retrievable session within the app or other app, or as part of ahealth kit implemented on the computing device. The app of thisdisclosure may, in some non-limiting examples, push the report to aproprietary network (e.g., via an online portal). By pushing the reportor other data in this way, the tools of this disclosure enable the HCPto review the patient's health information and enter data into anelectronic medical records (EMR) database or repository. The tools ofthis disclosure may generate a confirmation of receipt by the HCP basedon various criteria (and in some examples, an indication of whether thereport was reviewed by the HCP), and provide this communication to thepatient via communication to the mobile device or otherpatient-accessible computing modality. In some examples, the computingdevice may be configured to receive an indication as to whether thereport was reviewed by the HCP. In such examples, the computing devicemay be configured to provide, based at least in part on the indication,a user with a status of the report (e.g., HCP reviewed, reviewin-progress, etc.).

If the tools of this disclosure determine that any one check or anycombination of checks described above yielded an abnormal result (orabnormal outside an acceptable margin of normal), the tools of thisdisclosure may use the mobile device app to output a prompt to thepatient. In some examples, the prompt may indicate an abnormality. Insome examples, the prompt may include a recommendation or instruction toschedule a follow-up visit with the HCP.

In some examples, the tools of this disclosure may store sessioninformation (e.g., subsession information, combined session information,etc.) and results of previous checks (e.g., locally at the computingdevice, to a cloud storage resource, or to both). The computing devicemay do this in order to assist the patient and/or the HCP in tracking aprogress or changes with respect to the patient's wound recovery, thefunctioning of the medical device, etc. In some examples, the tools ofthis disclosure may implement a limited date counter with respect to theapp in order to deter or prevent the patient from continually using theapp after the follow-up time window has expired. Aspects of thisdisclosure enable bidirectional communication, e.g. to loop in thephysician, who may communicate with various messages, such as “call myoffice,” etc.

As would be appreciated by one of skill in the art, the remote medicaldevice monitoring, as disclosed herein, represents a significanttechnological advance over prior implementations. Specifically, thedisclosed techniques can identify and display a tailored set ofpatient-related content elements and additionally augmented content,resulting in an increased efficiency in accessing the relevant contentquickly and allowing user interactions with multiple patient-relatedcontent elements (e.g., content tiles, subsession interfaces, etc.).Further, the disclosed techniques can yield health benefits bydynamically determining the presence of abnormalities by using specifictools that are deployed and implemented at particular moments in orderto achieve the highest accuracy of an abnormality detection. A user mayalso be presented with the relevant control mechanisms that allow theuser to intuitively operate the UI in order to capture the correctimages, the correct physiological parameters, the correct deviceinterrogation data, and the correct patient-status updates, that will beused to achieve the highest accuracy of an abnormality detection. Assuch, the examples described herein represent significant improvementsin this computer-related technology.

Additional examples disclosed herein also represent improvements incomputer-related technology. For example, the camera system may useaugmented overlays (e.g., wireframes) in order to allow a user toaccurately align an implantation site or other part of the body of thepatient, such that images may be obtained in a consistent and reliablemanner. In addition, the use of such augmented overlays allows acomputing device to receive consistent images at particular anglesand/or with particular implantation-site sizes in order to determine aprogression of a healing of the patient over time based on an analysisof the images over time. Another technical improvement includes the useof stored images of a body of the patient (e.g., images taken shortlyafter surgery) in order to develop a wireframe (e.g., a transparentaugmented reality overlay) that emulates the implantation site, suchthat a patient may be able to accurately align the implantation sitewith the wireframe as a guide to capturing the image at a particularangle and/or size or with particular contrast, lighting, zoom, etc.Advantageously, the computing device may initiate an automaticstill-image capture of the image of the implantation site when, forexample, a healing scar at the implantation site fits within aparticular area of the wireframe. Additionally, the monitoring systemdisclosed herein may, in some examples, be native to other softwareapplications, and thus, the tiles may be displayed using similar UI/UXelements. Other example improvements include the ability to callexternal sources of information to provide a greater relevance andcomplexity to a post-implant report and in some examples, the ability tocall APIs (e.g., language translation APIs, machine learning APIs) toperform additional work within the monitoring system. In one example, acloud-deployed API may serve as the accessible endpoint for an ML model.In addition, various ML models or AI engines may be deployed asso-called light versions that are configured to operate efficiently ondevices with significantly limited resources (e.g., mobile devices,tablets, etc.).

The ideas disclosed herein exist within the realm of computer-relatedtechnology. For example, the display of information in a UI where therelevant data does not necessarily reside in a storage device locallycoupled to the relevant display device would be virtually impossible toreplicate outside the realm of computer-related technology. That is, insome instances, relevant data (e.g., training sets, physiologicalparameter data, images, etc.) may be stored on a cloud storage device,whereas in some examples, the relevant data may be stored locally (e.g.,on a mobile device of a patient) and/or synchronized with a cloudstorage device or another device, such as a mobile device of a HCP, at atime advantageous to the system so as to converse computing resources(e.g., processing, memory, power resources, etc.). In a non-limitingexample, the multi-faceted information representing, e.g., the staticcontent tiles, dynamically augmented content tiles, etc., may besimultaneously or dynamically obtained from disparate systems,identified by metadata in some instances, and simultaneously ordynamically presented in an interactive session UI and subsession UIs.Additionally, the monitoring system may intuitively present the data onthe proper display, through the proper medium, and/or at the propermoment (e.g., on a static schedule or dynamically-updating schedule). Inaddition, the techniques of this disclosure provide abnormalitydetermination techniques that may utilize, in some examples, non-visualsensors, such as thermal imaging with cameras in order to detecttemperature changes at an implantation site. In addition, various dataanalysis techniques are described that utilize a particular examples ofsynthesizing various data items (e.g., images of an implantation site,device status information, etc.), where the information sources may beoptimized to provide specific data relative to the specific technicalproblem of patient monitoring post-implant. That is, the synthesis ofdata, in some examples, provides a robust algorithm for identifyingabnormalities, notwithstanding the algorithms described for obtainingand analyzing specific sections of data, such as image data, forpotential abnormalities.

Additionally, it has been noted that design of computer UIs “that areuseable and easily learned by humans is a non-trivial problem forsoftware developers.” (Dillon, A. (2003) User Interface Design.MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan,453-458.). The various examples of interactive and dynamic UIs of thepresent disclosure are the result of significant research, development,improvement, iteration, and testing and in some examples, provide aparticular manner of obtaining information, summarizing, and presentinginformation in electronic devices. This non-trivial development hasresulted in the UIs described herein, which are likely to providesignificant cognitive and ergonomic efficiencies and advantages overprevious systems going forward. The interactive and dynamic UIs includeimproved human-computer interactions that may provide, for a user,reduced mental workloads/burdens, improved decision-making, reduced workstress, etc. For example, a UI with the interactive UIs described hereinmay provide an optimized presentation of patient-specific informationfrom various sources and may enable a user to more quickly access,navigate, assess, and utilize such information than with previoussystems which can be slow, complex and/or difficult to learn,particularly to novice users. Thus, the presentation of concise andcompact information on a particular UI that corresponds to a particularpatient makes for efficient use of the information available and optimaluse of the medical devices and virtual check-up functionalities of thisdisclosure.

FIG. 1 illustrates the environment of an example monitoring and/orcheck-in system 100 in conjunction with a patient 4. In some examples,system 100 may implement the various patient and medical devicemonitoring and abnormality detection techniques disclosed herein. System100 includes one or more medical device(s) 6 and one or more computingdevice(s) 2. While medical device(s) 6, in some instances, include anIMD, as shown in FIG. 1, the techniques of this disclosure are not solimited. For illustrative purposes, however, medical device(s) 6 may insome instances be referred to herein simply as IMD 6 or IMD(s) 6.

Computing device(s) 2 may be a computing device with a display viewableby a user. The user, may be a physician technician, surgeon,electrophysiologist, clinician (e.g., implanting clinician), or patient4. Patient 4 ordinarily, but not necessarily, will be a human. Forexample, patient 4 may be an animal needing ongoing monitoring forvarious health conditions (e.g., cardiac conditions, spinal cordconditions, etc.). In such instances, a human caregiver may operateaspects of the disclosed technology that utilize user input that may notbe feasibly available from an animal patient 4.

Computing device(s) 2 may be referred to in some instances herein as aplurality of “computing device(s) 2,” while in other instances may bereferred to simply as “computing device 2,” where appropriate. System100 may be implemented in any setting where at least one of computingdevice(s) 2 may interface with and/or monitor at least one of medicaldevice(s) 6 and/or an implantation site of medical device(s) 6, inaccordance with one or more techniques of this disclosure. Computingdevice(s) 2 may interface with and/or monitor medical device(s) 6, forexample, by imaging the implantation site of the medical device(s) 6, inaccordance with one or more techniques of this disclosure. In addition,computing device(s) 2 may interrogate medical device(s) 6 to obtain datafrom medical device(s) 6, such as performance data, historical datastored to memory, battery strength of the medical device(s) 6,impedance, pulse width, pacing percentage, pulse amplitude, pacing mode,internal device temperature, etc. In some examples, computing device(s)2 may perform an interrogation subsession with medical device(s) 6 byestablishing a wireless communication with one or more of the medicaldevice(s) 6. In some instances, medical device(s) 6 may or may notinclude an IMD. In an example, computing device(s) 2 interrogate thememory of a wearable medical device 6 in order to determine deviceoperating parameters as the interrogation data. In another example,computing device(s) 2 may receive (e.g., obtain) physiologicalparameters from medical device(s) 6, such as waveforms of aphysiological parameter, parameter labels (e.g., “abnormal ECGdetected”), etc. In another example, computing device(s) 2 may obtain apatient-status entered into computing device(s) 2 by a user (e.g.,patient 4, a caretaker of patient 4, etc.). In one example, the user mayenter the patient-status updates via a user interface of computingdevice(s) 2. In another example, the user may enter the patient-statusupdates via another computing device 2 that may then transferpatient-status data to one of computing device(s) 2 that is/areconfigured to operate an interactive check-in session. In such examples,network 10 or edge device(s) 12 may facilitate the exchange of databetween various computing device(s) 2, medical device(s) 6, etc., asdescribed in further detail with reference to FIG. 3.

In some examples, computing device(s) 2 may include one or more of acellular phone, a “smartphone,” a satellite phone, a notebook computer,a tablet computer, a wearable device, a computer workstation, one ormore servers, a personal digital assistant, a handheld computing device,virtual reality headsets, wireless access points, motion or presencesensor devices, or any other computing device that may run anapplication that enables the computing device to interact with medicaldevice(s) 6 or interact with another computing device that is, in turn,configured to interact with medical device(s) 6.

At least one of computing device(s) 2 may be configured to communicatewith medical device(s) 6 and, optionally, other ones of computingdevice(s) 2, via wired or wireless communication. Computing device(s) 2,for example, may communicate via near-field communication (NFC)technologies (e.g., inductive coupling, NFC or other communicationtechnologies operable at ranges less than 10-20 cm) and/or far-fieldcommunication technologies (e.g., Radio Frequency (RF) telemetryaccording to the 802.11, Bluetooth® specification sets, or othercommunication technologies operable at ranges greater than NFCtechnologies). In some examples, computing device(s) 2 may include aninterface for providing input to edge device(s) 12, network 10, and/ormedical device(s) 6. For example, computing device(s) 2 may include auser input mechanism, such as a touchscreen, that allows a user to storeimage(s) to a database. In such examples, one of edge device(s) 12 maymanage the database, which in some instances, computing device(s) 2 mayaccess via network 10 in order to perform one or more of the varioustechniques of this disclosure.

Computing device(s) 2 may include a user interface (UI) 22. In someexamples, UI 22 may be a graphical UI (GUI), an interactive UI, etc. Insome examples, UI 22 may further include a command line interface. Insome examples, computing device(s) 2 and/or edge device(s) 12 mayinclude a display system (not shown). In such examples, the displaysystem may comprise system software for generating UI data to bepresented for display and/or interaction. In some examples, processingcircuitry, such as that of computing device(s) 2, may receive UI datafrom another device, such as from one of edge device(s) 12 or server(s)94 (FIG. 3), that computing device(s) 2 may use to generate UI data tobe presented for display and/or interaction.

Computing device(s) 2 may be configured to receive, via UI 22, inputfrom the user. UI 22 may include, for example, a keypad and a display,which may for example, be a liquid crystal display (LCD) or lightemitting diode (LED) display. In some examples, a display of computingdevice(s) 2 may include a touch screen display, and a user may interactwith computing device(s) 2 via the display. It should be noted that theuser may also interact with computing device(s) 2 remotely via a networkcomputing device.

UI 22 may further include, in some examples, a keypad. In some examples,UI 22 may include together the keypad with the display. The keypad maytake the form of an alphanumeric keypad or a reduced set of keysassociated with particular functions. Computing device(s) 2 mayadditionally or alternatively include a peripheral pointing device, suchas a mouse, via which the user may interact with UI 22. In someinstances, UI 22 may include a UI that utilizes virtual reality (VR),augmented reality (AR), or mixed reality (MR) UIs, such as those thatmay be implemented via a VR, AR, or MR headset.

In some examples, processing circuitry, e.g., processing circuitry 20(FIG. 2) of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12 (FIG. 3), processing circuitry 98 of server(s) 94 (FIG. 3),or processing circuitry 40 of medical device(s) 17, may determineidentification data for patient 4, such as authentication data. In anexample, processing circuitry 20 may identify, based at least in part onthe identification data, IMD information that corresponds to IMD 6. Assuch, processing circuitry 20 may determine, based at least in part onthe IMD information, an interactive session program that defines one ormore parameters for imaging an implantation site, such as theimplantation site of patient 4. In one example, the interactive sessionprogram may define one or more parameters for imaging one or moreimplantation sites of patient 4. The interactive session program mayinclude the site-check subsession application, a patient-statussubsession application, etc. (e.g., mobile app(s) installed from an appstore). In some examples, the interactive session program may furtherinclude subsession process(es) (e.g., a site-check subsession imagingprogram, patient-status subsession program, physiological parametersubsession program, etc.) that are tailored to the user (e.g., patient4, HCP, etc.) of computing device(s) 2.

In an illustrative example, system 100 includes a system for monitoringpatient 4. The system includes a system of processors and storagedevices, as those elements are described further with reference to FIGS.2-4. In some examples, the storage devices may include a memory, such asa memory device of computing device(s) 2, where the memory may beconfigured to store image data (e.g., frames of image data, images of animplantation site, etc.). In addition, the storage devices may beconfigured to store additional data items, such as physiologicalparameter values, patient-status updates, device interrogation data,etc. The system of processors, which may include one or more processors,may be in communication with at least one of the storage devicesconfigured to store image data.

In some examples, one of computing device(s) 2, may include one or moreof the system of processors implemented in circuitry, where the one ormore processors may be configured to provide an interactive session,such as an virtual check-in interactive session, via computing device 2.In such examples, the virtual check-in interactive session may beconfigured to allow a user to navigate a plurality of subsessions (e.g.,via computing device 2). The plurality of subsessions may include atleast two subsessions as part of the interactive session. In oneexample, the plurality of subsessions may include at least a firstsubsession and a second subsession. In such examples, the secondsubsession is distinct from the first subsession. In addition, the firstsubsession may include a subsession that involves capturing the imagedata via one or more cameras. That is, the first subsession may includea site-check subsession. The second subsession may include adevice-check subsession, physiological parameter check subsession, apatient-status subsession, or other subsession configured to elicitinformation of patient 4 that would be useful in monitoring patient 4(e.g., an IMD patient). In some instances, the interactive session mayinclude a third and/or a fourth subsession, including one or more of adevice-check subsession, physiological parameter check subsession, apatient-status subsession, or other subsessions, as particular examplesubsessions are described herein.

In the illustrative example, computing device 2 may determine a firstset of data items in accordance with the first subsession of theinteractive session. In such examples, the first set of data itemsincludes the image data, such as frames of the image data. That is,computing device 2 may capture frames of image data via a camera (e.g.,camera 32 of FIG. 2) and may store the frames to memory. In one example,the image data may include a frame of a still-image of an implantationsite of medical device 6. In another example, the image data may includea frame of a still-image of areas of the body of patient 4 adjacent theimplantation site, such as where leads may be routed beneath the skin ofpatient 4. In some instances, computing device 2 may process the imagedata prior to storing the image data as the first set of data items. Inan example, computing device 2 may deploy an AI engine and/or ML modeltrained to identify abnormalities in images of implantation sites orother body parts of patient 4. AI engine and/or ML model may determineabnormality metrics, such as a likelihood (e.g., a confidence value) ofan abnormality at the implantation site, a type of abnormality, etc. Inany case, computing device 2 may determine the first set of data itemsto include the image data (e.g., abnormality determination, etc.).

In addition, computing device 2 may determine a second set of data itemsin accordance with the second subsession of the interactive session. Insuch instances, the second set of data items may be distinct from thefirst set of data items. This is because the second subsession includesa subsession configured to obtain complementary or supplemental datarelative to the first subsession, rather than to serve as a duplicate ofthe first subsession. To illustrate, the second set of data items mayinclude one or more of: interrogation data obtained from medicaldevice(s) 17 (e.g., IMD 6), one or more physiological parameters ofpatient 4, and/or user-input data. In such examples, computing device 2may determine, based at least in part on the first set of data items andthe second set of data items, an abnormality corresponding to at leastone of the patient or the IMD. In one example, the first set of dataitems and the second set of data items may indicate various abnormalitystates. The abnormality states may include device migration, a potentialinfection, abnormal healing, abnormal physiological parameters, abnormaldevice parameters, patient-input indicating a perceived abnormality,etc. In addition, the abnormality states may include healing granulationaround edges of an implantation site, discharge from an implantationsite, inflammation at the implantation site, tissue erosion at or aroundthe implantation site, etc. Thus, the abnormality corresponding topatient 4 or IMD 6 may include an abnormality determination based on thevarious abnormality states observed from the various data items.

In one example, computing device 2 may determine, from physiologicalparameters obtained via a second subsession, an ECG change thatindicates device migration and as such, increases the likelihood that apotential abnormality is being detected from the image data. In suchinstances, computing device 2 may analyze the image data using a biastoward detecting an abnormality or may include, in a post-implantreport, a heightened likelihood (e.g., probability, confidence interval)that is based on the likelihood of a potential abnormality determinedfrom the first and second set data items. Computing device(s) 2 mayoutput the post-implant report of the interactive session. In anillustrative example, the post-implant report may include an indicationof the abnormality and/or indication of an amount of time that hastranspired since the date of implantation of the IMD.

In some examples, computing device(s) 2 may include a programming heador paddle (not shown). In such examples, computing device(s) 2 mayinterface with medical device(s) 6 via the programming head. Theprogramming head may be placed proximate to the body of patient 4 nearmedical device(s) 6 (e.g., near an implantation site of IMD 6).Computing device(s) 2 may include a programming head in order to improvethe quality or security of communication between computing device(s) 2and medical device(s) 6. In addition, computing device(s) 2 may includea programming head in order to improve the quality or security ofcommunication between computing device(s) 2, medical device(s) 6, and/oredge device(s) 12.

In the illustrative and non-limiting example of FIG. 1, medicaldevice(s) 6 include at least one IMD. In such examples, the at least oneIMD may be implanted outside of a thoracic cavity of patient 4 (e.g.,subcutaneously in the pectoral location illustrated in FIG. 1). In someexamples, medical device(s) 6 may be positioned near the sternum near orjust below the level of the heart of patient 4, e.g., at least partiallywithin the cardiac silhouette. As used herein, an IMD may include, be,or be part of a variety of devices or integrated systems, such as, butnot limited to, implantable cardiac monitors (ICMs), implantablepacemakers, including those that deliver cardiac resynchronizationtherapy (CRT), implantable cardioverter-defibrillators (ICDs),diagnostic devices, cardiac devices, etc. In some examples, the tools ofthis disclosure may be configured to monitor functioning of or useradaptation to implants other than CIEDs, such as spinal cordstimulators, deep brain stimulators, gastrological stimulators,urological stimulators, other neurostimulators, orthopedic implants,respiratory monitoring implants, etc.

In some examples, medical device(s) 6 may include one or more CIEDs. Insome examples, patient 4 may interface with multiple medical device(s)6, concurrently. In an illustrative example, patient 4 may have multipleIMDs implanted within the body of patient 4. In another example, medicaldevice(s) 6 may include a combination of one or more implanted and/ornon-implanted medical devices. An example of a non-implanted medicaldevice includes a wearable device (e.g., monitoring watch, wearabledefibrillator, etc.) or any other external medical devices configured toobtain physiological data of patient 4.

In some examples, medical device(s) 6 may include diagnostic medicaldevices. In an example, medical device(s) 6 may include a device thatpredicts heart failure events or that detects worsening heart failure ofpatient 4. In a non-limiting and illustrative example, system 100 may beconfigured to measure impedance fluctuations of patient 4 and processimpedance data to accumulate evidence of worsening heart failure. In anycase, medical device(s) 6 may be configured to determine a health statusrelating to patient 4. Medical device(s) 6 may transmit the diagnosticdata or health status to computing device(s) 2 as interrogation data,such that computing device(s) 2 may correlate the interrogation datawith image data to determine whether an abnormality present with aparticular one of medical device(s) 6 (e.g., an IMD) or patient 4 (e.g.,infection at an implantation site).

In some examples, medical device(s) 6 may operate as a therapy deliverydevice. For example, medical device(s) may deliver electrical signals tothe heart of patient 4, such as an implantable pacemaker, acardioverter, and/or defibrillator, a drug delivery device that deliverstherapeutic substances to patient 4 via one or more catheters, or as acombination therapy device that delivers both electrical signals andtherapeutic substances. As described herein, computing device(s) 2 maydetermine various interactive session programs or at least aspects of aninteractive session program (e.g., imaging programs, UI programs, etc.),based the type of medical device implanted in patient 4 or based onidentifying information of patient 4.

It should be noted that, while certain example medical device(s) 6 aredescribed as being configured to monitor cardiovascular health, thetechniques of this disclosure are not so limited, and persons skilled inthe art will understand that the techniques of this disclosure may beimplemented in other contexts (e.g., neurological, orthopedic, etc.). Insome examples, one or more of medical device(s) 6 may be configured toperform deep brain stimulation (DBS), spinal cord stimulation (SCS),pelvic stimulation, peripheral nerve stimulation, muscle stimulation,etc.

In addition, while certain example medical device(s) 6 are described asbeing insertable or implantable devices, the techniques of thisdisclosure are not so limited, and persons skilled in the art willunderstand that the techniques of this disclosure may be implementedwith medical device(s) 6 that are not configured to be insertable orimplantable, such as wearable devices or other external medical devices.In a non-limiting example, medical device(s) 6 may include wearabledevices (e.g., smart watches, headsets, etc.) configured to obtainphysiological data (e.g., activity data, heart rate, etc.) and transfersuch data to computing device(s) 2, network 10, edge device(s) 12, etc.for subsequent utilization, in accordance with one or more of thevarious techniques of this disclosure.

Moreover, while certain example medical device(s) 6 are described asbeing electrical devices or electrically-active devices, the techniquesof this disclosure are not so limited, and person skilled in the artwill understand that, in some examples, medical device(s) 6 may includenon-electrical or non-electrically-active devices (e.g., orthopedicimplants, etc.). In any case, medical device(s) 6 may be configured tocommunicate medical data to computing device(s) 2, such as via atelemetry protocol, Radio-Frequency Identification (RFID) transmission,etc. As such, any medical device and/or computing device configured tocommunicate medical data may be configured to implement the techniquesof this disclosure.

In some examples, medical device(s) 6 may be implanted subcutaneously inpatient 4. Furthermore, in some examples, computing device(s) 2 maymonitor subcutaneous impedance values obtained from medical device(s) 6.In some examples, at least one of medical device(s) 6 takes the form ofthe Reveal LINQ™ Insertable Cardiac Monitor (ICM), or another ICMsimilar to, e.g., a version or modification of, the LINQ™ ICM, developedby Medtronic, Inc., of Minneapolis, Minn. In such examples, medicaldevice(s) 6 may facilitate relatively longer-term monitoring of patientsduring normal daily activities.

In a non-limiting example, medical device(s) 6 may include an IMD thatis configured to operate as a pacemaker, a cardioverter, and/ordefibrillator, or otherwise monitor the electrical activity of the heartof patient 4. In some examples, medical device(s) 6 can provide pacingpulses to the heart of patient 4 based on the electrical signals sensedwithin the heart of patient 4.

In some examples, medical device(s) 6 may also provide defibrillationtherapy and/or cardioversion therapy via electrodes located on at leastone lead and/or a housing electrode. Medical device(s) 6 may detectarrhythmia of the heart of patient 4, such as fibrillation ofventricles, and deliver defibrillation therapy to the heart of patient 4in the form of electrical pulses. In some examples, medical device(s) 6may be configured to deliver a progression of therapies, e.g., pulseswith increasing energy levels, until a fibrillation of the heart ofpatient 4 is stopped. In such examples, medical device(s) 6 detectsfibrillation employing one or more fibrillation detection techniquesknown in the art.

In some examples, system 100 may be implemented in a setting thatincludes network 10 and/or edge device(s) 12. That is, in some examples,system 100 may operate in the context of network 10 and/or include oneor more edge device(s) 12. In some instances, network 10 may includeedge device(s) 12. Similarly, computing device(s) 2 may includefunctionality of edge device(s) 12 and thus, may also serve as one ofedge device(s) 12.

In some examples, edge device(s) 12 include modems, routers, Internet ofThings (IoT) devices or systems, smart speakers, screen-enhanced smartspeakers, personal assistant devices, etc. In addition, edge device(s)12 may include user-facing or client devices, such as smartphones,tablet computers, personal digital assistants (PDAs), and other mobilecomputing devices.

In examples involving network 10 and/or edge device(s) 12, system 100may be implemented in a home setting, a hospital setting, or in anysetting comprising network 10 and/or edge device(s) 12. The exampletechniques may be used with medical device(s) 6, which may be inwireless communication with one or more edge device(s) 12 and otherdevices not pictured in FIG. 1 (e.g., network servers).

In some examples, computing device(s) 2 may be configured to communicatewith one or more of medical device(s) 6, edge device(s) 12, or network10 operating a network service such as the Medtronic CareLink® Networkdeveloped by Medtronic, Inc., of Minneapolis, Minn. In some examples,medical device(s) 6 may communicate, via Bluetooth®, with computingdevice(s) 2. In some instances, network 10 may include one or more ofedge device(s) 12. Network 10 may be and/or include any appropriatenetwork, including a private network, a personal area network, anintranet, a local area network (LAN), a wide area network, a cablenetwork, a satellite network, a cellular network, a peer-to-peernetwork, a global network (e.g., the Internet), a cloud network, an edgenetwork, a network of Bluetooth® devices, etc., or a combinationthereof, some or all of which may or may not have access to and/or fromthe Internet. That is, in some examples, network 10 comprises theInternet. In an illustrative example, computing device(s) 2 mayperiodically transmit and/or receive various data items, via network 10,to and/or from one of medical device(s) 6, and/or edge device(s) 12.

In addition, computing device(s) 2 may be configured to connect tocellular base station transceivers (e.g., for 3G, 4G, LTE, and/or 5Gcellular network access), and Wi-Fi™ access points, as those connectionsare available. In some examples, the cellular base station transceiversmay have connections that provide access to network 10. These variouscellular and Wi-Fi™ network connections can be managed by differentthird-party entities, referred to herein as “carriers.”

In some examples, system 100 may include one or more databases (e.g.,storage device 96) that store various medical data records, cohort data,image data. In such examples, server(s) 94 (e.g., one or more databases)may be managed or controlled by one or more separate entities (e.g.,internet service providers (ISPs), etc.).

Computing device(s) 2 and/or edge device(s) 12 may be used to configureoperational parameters for medical device(s) 6. In some examples, theuser may use computing device(s) 2 as a programmer to programmeasurement parameters, stimulation programs, etc. Computing device(s) 2may also be configured to program a therapy progression, selectelectrodes to deliver defibrillation pulses, select waveforms for adefibrillation pulse or stimulation pulse train, select or configure afibrillation detection algorithm, etc. The user may also use computingdevice(s) 2 to program aspects of other therapies that may be providedby medical device(s) 6, such as cardioversion or pacing therapies. Insome examples, the user may activate certain features of medicaldevice(s) 6 by entering a single command via UI 22 of computingdevice(s) 2, such as depression of a single key or combination of keysof a keypad or a single point-and-select action with a pointing device.In addition, computing device(s) 2 may operate the interactive sessionof this disclosure, where the interactive session is loaded with theprogramming parameters. Computing device(s) 2 may utilize such data fordetermining physiological parameters and/or interrogation data thatdeviates from an expected value as expected from the programmingparameters. Computing device(s) 2 may utilize an AI engine and/or MLmodel in order to determine such deviations (e.g., abnormalities), whereAI engine and/or ML model may be trained on programming parameters andon abnormality data in order to determine correlations between such dataitems.

In some examples, computing device(s) 2 may be configured to retrievedata from medical device(s) 6. The retrieved data may include values ofphysiological parameters measured by medical device(s) 6, indications ofepisodes of arrhythmia or other maladies detected by medical device(s)6, and physiological signals obtained by medical device(s) 6. In someexamples, computing device(s) 2 may retrieve cardiac EGM segmentsrecorded by computing device(s) 2, e.g., due to computing device(s) 2determining that an episode of arrhythmia or another malady occurredduring the segment, or in response to a request, from patient 4 oranother user, to record the segment.

In some examples, the user may also use computing device(s) 2 toretrieve information from medical device(s) 6 regarding other sensedphysiological parameters of patient 4, such as activity or posture. Insome examples, edge device(s) 12 may interact with medical device(s) 6in a manner similar to computing device(s) 2, e.g., to program medicaldevice(s) 6 and/or retrieve data from medical device(s) 6.

Processing circuitry of system 100, e.g., of medical device(s) 6,computing device(s) 2, edge device(s) 12, and/or of one or more othercomputing devices (e.g., remote servers), may be configured to performthe example techniques of this disclosure for determining an abnormalitystatus of patient 4 and/or of components of IMD 6. The processingcircuitry, may be referred to herein in some instances as a system ofprocessors or a system of processing circuitry. In some examples, thesystem of processing circuitry of system 100 obtains physiologicalparameters, images, medical device diagnostics, etc. to determinewhether to provide an alert to patient 4 and/or a HCP.

In some instances, processing circuitry of system 100, e.g., ofcomputing device(s) 2, provides an alert to patient 4 and/or other userswhen combination of patient health data (e.g., implantation site images,ECG parameters, etc.), medical device diagnostic data, and indicates theonset of an abnormality. The alert may be an audible alert generated bymedical device(s) 6 and/or computing device(s) 2, a visual alertgenerated by computing device(s) 2, such as a text prompt or flashingbuttons or screen, or a tactile alert generated by medical device(s) 6and/or computing device(s) 2 such as a vibration or vibrational pattern.Furthermore, the alert may be provided to other devices, e.g., vianetwork 10. Several different levels of alerts may be used based on aseverity of a potential infection detected in accordance with one ormore of the various techniques disclosed herein.

In some instances, processing circuitry of system 100, e.g., ofcomputing device(s) 2, provides an alert to patient 4 and/or other userswhen a combination of patient health data (e.g., implantation siteimages, ECG parameters, etc.), medical device diagnostic data, andindicates the onset of an abnormality. The process for determining whento alert patient 4 involves measuring an abnormality (e.g., severity orprobability levels) against one or more threshold values and isdescribed in greater detail below. The alert may be an audible alertgenerated by medical device(s) 6 and/or computing device(s) 2, a visualalert generated by computing device(s) 2, such as a text prompt orflashing buttons or screen, or a tactile alert generated by medicaldevice(s) 6 and/or computing device(s) 2 such as a vibration orvibrational pattern. Furthermore, the alert may be provided to otherdevices, e.g., via network 10. Several different levels of alerts may beused based on a severity of a potential abnormality detected through thetechniques disclosed herein.

FIG. 2 is a block diagram illustrating an example configuration ofcomponents of at least one computing device 2 of computing device(s) 2.In the example of FIG. 2, the at least one computing device 2 includesprocessing circuitry 20, communication circuitry 26, storage device 24,and UI 22.

Processing circuitry 20 may include one or more processors that areconfigured to implement functionality and/or process instructions forexecution within computing device(s) 2. For example, processingcircuitry 20 may be capable of processing instructions stored in storagedevice 24. Processing circuitry 20 may include, for example,microprocessors, a digital signal processors (DSPs), an applicationspecific integrated circuits (ASICs), field-programmable gate arrays(FPGAs), complex programmable logic devices (CPLDs), or equivalentintegrated or discrete logic circuitry, or a combination of any of theforegoing devices or circuitry. Accordingly, processing circuitry 20 mayinclude any suitable structure, whether in hardware, software, firmware,or any combination thereof, to perform the functions ascribed herein toprocessing circuitry 20.

A trained ML model 30 and/or AI engine 28 may be configured to processand analyze the user input (e.g., images of the implantation site,patient status data, etc.), device parameters (e.g., accelerometerdata), historical data of medical device (e.g., medical device 6),and/or physiological parameters, in accordance with certain examples ofthis disclosure where ML models are considered advantageous (e.g.,predictive modeling, inference detection, contextual matching, naturallanguage processing, etc.). Examples of ML models and/or AI engines thatmay be so configured to perform aspects of this disclosure includeclassifiers and non-classification ML models, artificial neural networks(“NNs”), linear regression models, logistic regression models, decisiontrees, support vector machines (“SVM”), Naïve or a non-Naïve Bayesnetwork, k-nearest neighbors (“KNN”) models, deep learning (DL) models,k-means models, clustering models, random forest models, or anycombination thereof. Depending on the implementation, the ML models maybe supervised, unsupervised or in some instances, a hybrid combination(e.g., semi-supervised). These models may be trained based on dataindicating how users (e.g., patient 4) interact with computing device(s)2. For example, certain aspects of the disclosure will be describedusing events or behaviors (such as clicking, viewing, or watching) withrespect to items (e.g., wound images, cameras, videos, physiologicalparameters, etc.), for purposes of illustration only. In anotherexample, these models and engines may be trained to synthesize data inorder to identify abnormalities of patient 4 or medical device(s) 17 andto identify abnormalities of patient 4 or medical device(s) 17 fromindividual data items.

In a non-limiting example, patient 4 may exhibit difficulty withcapturing images of the implantation site from various angles. Thishelpful data may be shared across a health monitoring or computingnetwork so that optimal results may be presented to more than one userbased on similar queries and user reactions to those queries. Forbrevity, these aspects may not be described with respect to events orbehaviors regarding objects (e.g., data objects, such as searchstrings). In some examples, processing circuitry 40 may use MLalgorithms (e.g., DL algorithms) to, for example, monitor a progressionof a wound that is healing or predict that a potential infection isoccurring, for example, with respect to an implant site of one ofmedical device(s) 17. In an illustrative and non-limiting example, AIengine(s) 28 and/or ML model(s) 30 may utilize a deep-neural network tolocalize an implantation site in an image and classify an abnormalitystatus. In another example, AI engine(s) 28 and/or ML model(s) 30 mayutilize Naïve Bayes and/or decision trees to synthesize (e.g., combine)data items and the analysis thereof (e.g., image analysis and ECGanalysis) in order to obtain a comprehensive abnormality determinationfor patient 4 and include such comprehensive determinations in a report,such as for patient 4.

In another example, AI engine(s) 28 and/or ML model(s) 30 may be loadedwith and trained on cohort parameters (e.g., age cohorts, IMD typecohorts, skin pigmentation cohorts, etc.) and combinations of cohortparameters. In such examples, AI engine(s) 28 and/or ML model(s) 30 mayutilize historical reference interrogations for comparison whendetermining deviations from baseline parameters.

In addition, computing device(s) 2 may utilize different imageprocessing algorithms and/or data synthesis algorithms, such as AI, ML,DL, digital signal processing, neural networks, and/or other techniques,depending on various different contexts and other various differentresource constraints (e.g., processing power, network access, batterylife, etc.).

In some examples, AI engine(s) 28 may be trained to analyze the gait ofa patient with an orthopedic implant in place (e.g., by comparing videodata representing the patient against cohort gait information). In suchexamples, computing device(s) 2 may deploy AI engine(s) 28 and/or MLmodel(s) 30 to analyze patient activity in order to determine thepresence of a potential abnormality with patient 4 and/or medicaldevice(s) 17 (e.g., IMD 6), such as when the gait of the patient appearsabnormal to AI engine(s) 28 and/or ML model(s) 30 and/or when the gaitof the patient changes over time so as to represent a developing orsudden abnormality of patient 4.

In addition, trained AI engine(s) 28 may be used to learn about patient4 over time and learn about an implantation site of a patient 4. In thisway, AI engine(s) 28 may provide a personalized detection algorithm fordetected abnormalities for a particular implantation site. In suchexamples, AI engine(s) 28 may be loaded with and trained on cohortparameters, using historical reference interrogations or images forcomparison. In this way, computing device(s) 2 may provide a solutionusing different algorithm approaches (e.g., AI, ML, DL, digital signalprocessing, neural networks, and/or other techniques) in order topersonalize the site-check process for patient 4.

A user, such as a clinician or patient 4, may interact with one or moreof computing device(s) 2 through UI 22. UI 22 includes a display (notshown), such as a liquid crystal display (LCD) or a light emitting diode(LED) display or other type of screen, with which processing circuitry20 may present health- or device-related information, e.g., cardiacEGMs, indications of detections of impedance changes, temperaturechanges, etc. In addition, UI 22 may include an input mechanism toreceive input from the user. The input mechanisms may include, forexample, any one or more of buttons, a keypad (e.g., an alphanumerickeypad), a peripheral pointing device, a touch screen, or another inputmechanism that allows the user to navigate through UI 22 presented byprocessing circuitry 20 of computing device(s) 2 and provide input. Inone examples, UI 22 may allow a user to rotate images and adjust zoomlevels using a touch screen of computing device 2 (e.g., pinch-to-zoom,gestures, gaze tracking, etc.). In addition, UI 22 may allow the user tocontrol camera parameters, such as to select a forward or rear facingcamera, lighting, zoom levels, focus levels, contrast, etc., such thatthe user may capture images according to the camera parameters. Inanother example, computing device(s) 2 may automatically adjust suchparameters, with or without initial input from the user.

In some examples, computing device(s) 2 may include an imaging device.In an illustrative example, computing device(s) 2 may include a camera32 or multiple cameras 32 (e.g., digital cameras), as example imagingdevice(s). As shown in FIG. 2, camera 32 may refer to a collectivedevice including one or more image sensor(s) 34, one or more lens(es)36, and one or more camera processor(s) 38 (e.g., image signalprocessor). In some examples, processing circuitry 20 may include cameraprocessor(s) 38.

In some examples, computing device(s) 2 may include an imaging device,such as a camera 32. Camera 32 may be a digital camera built intocomputing device(s) 2. In some examples, camera 32 may be separate fromcomputing device(s) 2. In such examples, camera 32 may communicateimaging data to computing device(s) 2 and/or other computing device(e.g., edge device(s) 2). In some examples, computing device(s) 2 mayinclude a charge-coupled device (CCD) chip. The CCD chip may beconfigured to operate in a spectral response analysis mode using flashas an excitation (e.g., white light) light source. In such examples,computing device(s) 2 may employ a CCD chip in order to analyze an imageof an implantation site. In one example, computing device(s) 2 mayemploy a CCD chip to provide separate color filtering analysis indifferent light wavelength to better detect redness and swelling indifferent skin tones. That is, computing device(s) 2 may perform a colorfiltering on the images in order to identify an abnormality from one ormore image(s). In another example, computing device(s) 2 may perform acolor filtering on an image in order to identify an abnormality from oneor more image(s). In an illustrative example, computing device(s) 2 mayperform, in accordance with one or more of the various techniques ofthis disclosure, regional comparison of bodily zones for differentiatingan implantation site from another area of the body and for differentskin types (e.g., skin tones).

In some examples, computing device(s) 2 may use camera 32 to image animplantation site of patient 4. In such examples, a storage device 24 ofcomputing device 2 may store image data (e.g., still-images, etc.). Insuch instances, processing circuitry, such as processing circuitry 20 ofcomputing device 2 and/or processing circuitry 64 (FIG. 3) of edgedevice(s) 12, may be in communication with storage device 24.

In some examples, multiple cameras 32 may be included with a single oneof computing device(s) 2 (e.g., a mobile phone having one or more frontfacing cameras and one or more rear facing cameras). In some examples,computing device(s) 2 may include a first camera 32 having one or moreimage sensors 34 and one or more lenses 36, and a second camera 32having one or more image sensors 34 and one or more lenses 36, etc. Itshould be noted that while some example techniques herein may bediscussed in reference to frames received from a single camera (e.g.,from a single image sensor), the techniques of this disclosure are notso limited, and a person of skill in the art will appreciate that thetechniques of this disclosure may be implemented for any type of camera32 and combination of cameras 32, such as a combination of cameras 32that may be included with computing device(s) 2 or otherwise,communicatively coupled to computing device(s) 2. In some examples,image sensor(s) 34 represent one or more image sensors 34 that mayinclude image-sensor processing circuitry. In some examples, imagesensor(s) 34 include an array of pixel sensors (e.g., pixels) forcapturing representations of light.

While shown as being optionally included with computing device(s) 2(e.g., via dashed lines), the techniques of this disclosure are not solimited, and in some instances, camera 32 may be separate from computingdevice(s) 2, such as a stand-alone camera device or separate camerasystem. In any case, camera 32 may be configured to capture an image ofan implantation site and transfer the image data to processing circuitry20 via camera processor(s) 38. In some examples, camera 32 may beconfigured to achieve various zoom levels. In one example, camera 32 maybe configured to perform cropping and/or scaling techniques to achieve aparticular zoom level. In some examples, camera 32 may be configured tomanipulate an output from image sensor(s) 34 and/or manipulate lens(es)36 in order to achieve the particular zoom level.

In some examples, computing device(s) 2 may include audio circuitry (notshown) for providing audible notifications, instructions or other soundsto the user, receiving voice commands from the user, or both. In someexamples, computing device(s) 2 may provide audible notificationsindicating to a user a direction to proceed through UI 22 and/or thevirtual check-in process. In addition, computing device(s) 2 may provideaudible notifications that indicate when a task is complete, such aswhen an authentication task (e.g., successful login), when an ECGmeasurement task is complete, or when any other task is complete. Inanother example, computing device(s) 2 may provide different audiblenotifications depending on particular outcomes. In one example,computing device(s) 2 may provide a quiet notification when the outcomeof the interactive session is such that no follow-up appointment iswarranted (e.g., no potential infection, device operating correctly,good physiological parameters, etc.), whereas computing device(s) 2 mayprovide a louder notification when the outcome of the interactivesession indicates that a follow-up appointment is advised. In oneexample, computing device(s) 2 may provide an audible notification whenthe possibility of one or more abnormalities is identified. In anotherexample, computing device(s) 2 may provide an audible notification uponidentifying a collective abnormality (e.g., following a synthesis ofdata). In an illustrative example, computing device(s) 2 may determine acollective abnormality based on an identified potential abnormality atthe implantation site and based on an abnormality of a physiologicalparameter (e.g., an ECG abnormality). In some instances, computingdevice(s) 2 may nevertheless determine the presence of a potentiallyhealth-threatening abnormality when no abnormality is identified in onesubsession (e.g., an implantation site abnormality) but that anabnormality is identified in another subsession (e.g., an IMDabnormality), such that an analysis of the at least two subsessionindicates that there is or is not an abnormality that warrants afollow-up appointment advisement. In addition, computing device(s) 2 mayprovide different audible notifications following each subsession andthen again, following the completion of all proscribed subsessions(e.g., two subsessions, three subsessions, etc.).

Communication circuitry 26 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as medical device(s) 6. Under the control of processingcircuitry 20, communication circuitry 26 may receive downlink telemetryfrom, as well as send uplink telemetry to, medical device(s) 6, oranother device. Communication circuitry 26 may be configured to transmitor receive signals via inductive coupling, electromagnetic coupling,NFC, RF communication, Bluetooth®, Wi-Fi™, or other proprietary ornon-proprietary wireless communication schemes. Communication circuitry26 may also be configured to communicate with devices other than medicaldevice(s) 6 via any of a variety of forms of wired and/or wirelesscommunication and/or network protocols. In some examples, computingdevice(s) 2 may perform telemetry selection through a sweep (e.g., TelC,TelB, TelM, Bluetooth®, etc.).

Storage device 24 may be configured to store information withincomputing device(s) 2 during operation. Storage device 24 may include acomputer-readable storage medium or computer-readable storage device. Insome examples, storage device 24 includes one or more of a short-termmemory or a long-term memory. Storage device 24 may include, forexample, read-only memory (ROM), random access memory (RAM),non-volatile RAM (NVRAM), Dynamic RAM (DRAM), Static RAM (SRAM),magnetic discs, optical discs, flash memory, forms ofelectrically-erasable programmable ROM (EEPROM) or erasable programmableROM (EPROM), or any other digital media.

In some examples, storage device 24 is used to store data indicative ofinstructions for execution by processing circuitry 20. In addition,storage device 24 may store image data and/or supplemental image data.In some examples, storage device 24 may store frames of image data. Thatis, storage device 24 may store one or more images. In some examples,storage device 24 may store one or more images of a body of patient 4(e.g., implantation site images, skin images for skin color analytics,skin surfaces proximate lead routings, etc.), physiological parameterimages (e.g., ECG images), etc. Storage device 24 may be used bysoftware or applications running on computing device(s) 2 to temporarilystore information during program execution. Storage device 24 may alsostore historical medical device data, historical patient data, timinginformation (e.g., number of days since implantation of an IMD, numberof days since a particular physiological parameter has been above acertain threshold, etc.), AI and/or ML training sets, image data, etc.

Data exchanged between computing device(s) 2, edge device(s) 12, network10, and medical device(s) 6 may include operational parameters ofmedical device(s) 6. Computing device(s) 2 may transmit data, includingcomputer-readable instructions, to medical device(s) 6. Medicaldevice(s) 6 may receive and implement the computer-readableinstructions. In some examples, the computer-readable instructions, whenimplemented by medical device(s) 6, may control medical device(s) 6 tochange one or more operational parameters, export collected data, etc.In an illustrative example, processing circuitry 20 may transmit aninstruction to medical device(s) 6 which requests medical device(s) 6 toexport collected data (e.g., ECGs, impedance values, etc.) to computingdevice(s) 2, edge device(s) 12, and/or network 10. In turn, computingdevice(s) 2, edge device(s) 12, and/or network 10 may receive thecollected data from medical device(s) 6 and store the collected data,for example, in storage device 24. In addition, processing circuitry 20may transmit an interrogation instruction to medical device(s) 6 whichrequests medical device(s) 6 to export operational parameters (e.g.,battery, impedance, pulse width, pacing %, etc.).

In some examples, computing device(s) 2 may be coupled to externalelectrodes, or to implanted electrodes via percutaneous leads. In suchexamples, computing device(s) 2 may receive, from medical device(s) 6,and monitor physiological parameters, ECGs, etc., according to one ormore techniques disclosed herein.

In the example illustrated in FIG. 2, processing circuitry 20 isconfigured to perform the various techniques described herein, such asthe techniques described with reference to FIGS. 5-22. To avoidconfusion, processing circuitry 20 is described as performing thevarious processing techniques proscribed to computing device(s) 2, butit should be understood that at least some of these techniques may alsobe performed by other processing circuitry (e.g., processing circuitry40 of medical device(s) 17 (FIG. 4), processing circuitry 98 ofserver(s) 94, processing circuitry 64 of edge device(s) 12, etc.). In anillustrative example, processing circuitry 20 may capture image(s) of animplantation site, obtain other data items, output images and/or otherdata items, for infection detection, to an analysis platform, determine,based on the images and other data items, presence of potentialabnormality (e.g., an infection), output a summary of an implantationstatus including potential abnormality information, and in someinstances, transmit the summary of the implantation status including thepotential infection information to another device. In this example, theanalysis platform may be a separate program that processing circuitry 20executes. In another example, the analysis platform may be a separateprogram that other processing circuitry from the system of processorsexecutes instead.

FIG. 3 is a block diagram illustrating an example system 300 thatincludes one or more example computing device(s) 2, one or more medicaldevice(s) 17, network 10, one or more edge device(s) 12, and one or moreserver(s) 94, in accordance with one or more techniques disclosedherein. In some examples, system 300 is an example of system 100described with reference to FIG. 1. In another example, system 300illustrates an example network system that hosts monitoring system 100.In some examples, medical device(s) 17 may be an example of medicaldevice(s) 6 of FIG. 1. That is, medical device(s) 17 may include an IMD,CIED, etc., similar to that as described with reference to FIG. 1. Inaddition, medical device(s) 17 may include non-implantable medicaldevices, including wearable medical devices (e.g., smart watches,wearable defibrillators, etc.).

Medical device(s) 17 may be configured to transmit data, such as sensed,measured, and/or determined values of physiological parameters (e.g.,heart rates, impedance measurements, fluid indices, respiratory rate,activity data, cardiac electrograms (EGMs), historical physiologicaldata, blood pressure values, etc.), to edge device(s) 12, computingdevice(s) 2 and/or an access point (e.g., gateway). In some examples,medical device(s) 17 may be configured to determine multiplephysiological parameters. For example, medical device(s) 17 may includemedical device(s) 6 (e.g., an IMD) configured to determine respirationrate values, subcutaneous tissue impedance values, EGM values, etc. Edgedevice(s) 12 and/or an access point device may then communicate, vianetwork 10, the retrieved data to server(s) 94.

In some examples, medical device(s) 17 may transmit data over a wired orwireless connection to server(s) 94, edge device(s) 12, or computingdevice(s) 2. For example, server(s) 94 may receive data from medicaldevice(s) 17 (e.g., IMD(s) 6, wearable devices, etc.) or from edgedevice(s) 12. In another example, edge device(s) 12 may receive, vianetwork 10, data from server(s) 94. In some examples, edge device(s) 12may receive, via network 10 or via a wired or wireless connection, datafrom medical device(s) 17. In such examples, edge device(s) 12 maydetermine the data received from server(s) 94, medical device(s) 17, orcomputing device(s) 2. In some examples, edge device(s) 12 may store thedata to a storage device 62 internal to edge device(s) 12. Processingcircuitry 64 of edge device(s) 12 may also include AI engines and/or MLmodels, such as the AI engines and ML models described with reference toFIG. 2.

In this example, medical device(s) 17 may use communication circuitry 42to communicate with one of edge device(s) 12 via a first wirelessconnection. In some examples, medical device(s) 17 may use communicationcircuitry 42 to communicate with an access point via a second wirelessconnection. The access point may include a device that connects tonetwork 10 via any of a variety of connections, such as telephonedial-up, digital subscriber line (DSL), or cable modem connections. Insome examples, an access point may be coupled to network 10 throughdifferent forms of connections, including wired or wireless connections.In some examples, one of computing device(s) 2 may serve as an accesspoint for network 10. For example, a user device, such as a tablet orsmartphone, may be co-located with patient 4 and may be configured toserve as an access point. In any case, computing device(s) 2, edgedevice(s) 12, and server(s) 94 are interconnected and may communicatewith each other through network 10.

Medical device(s) 17, edge device(s) 12, and/or computing device(s) 2may be configured to communicate, via various connections, over network10 with remote computing resources (e.g., server(s) 94). The datanetwork (e.g., network 10) may be implemented by server(s) 94 (e.g.,data servers, remove servers, analytic servers, etc.). In an example,server(s) 94 may include a data server configured to store data and/orperform computations based on the data. In another example, server(s) 94may include a data server configured to store data (e.g., a database)and send data to another one of server(s) 94 for data analytics, imageprocessing, or other data computations, in accordance with one or moreof the various techniques of this disclosure. In some examples,server(s) 94 may be implemented on one or more host devices, such asblade servers, midrange computing devices, mainframe computers, desktopcomputers, or any other computing device configured to provide computingservices and resources. Protocols and components for communicating viathe Internet or any of the other aforementioned types of communicationnetworks are known to those skilled in the art of computercommunications and thus, need not be described in more detail herein.

In some examples, one or more of medical device(s) 17 may serve as orinclude server(s) 94. That is, medical device(s) 17 may include enoughstorage capacity or processing power to perform the techniques disclosedherein on a single one of medical device(s) 17 or on a network ofmedical device(s) 17 coordinating tasks via network 10 (e.g., over aprivate or closed network). In some examples, one of medical device(s)17 may include at least one of server(s) 94. For example, aportable/bedside patient monitor may be configured to serve as one ofserver(s) 94, as well as serving as one of medical device(s) 17configured to obtain physiological parameter values from patient 4.

In some examples, server(s) 94 may communicate with each of medicaldevice(s) 17, via a wired or wireless connection, to receivephysiological parameter values from medical device(s) 17 and/or deviceinterrogation data from medical device(s) 17. In a non-limiting example,physiological parameter values and/or device interrogation data may betransferred from medical device(s) 17 to server(s) 94 and/or to edgedevice(s) 12. Server(s) 94 and/or edge device(s) 12 may perform analysison the data to determine the presence of an abnormality at animplantation site (e.g., at a location of the body of patient 4 in whichone or more IMD components coincide). Server(s) 94 and/or edge device(s)12 may transmit, via communication circuitry, a result of the analysisto computing device(s) 2 for display and/or further processing.

In some examples, server(s) 94 may be configured to provide a securestorage site for data that has been collected from medical device(s) 17,edge device(s) 12, and/or computing device(s) 2. In some instances,server(s) 94 may comprise a database that stores medical- andhealth-related data. For example, server(s) 94 may comprise a cloudserver or other remote server that stores data collected from medicaldevice(s) 17, edge device(s) 12, and/or computing device(s) 2. In somecases, server(s) 94 may assemble data in web pages or other documentsfor viewing by trained professionals, such as clinicians, via computingdevice(s) 2. In the example illustrated by FIG. 3, server(s) 94 includesa storage device 96 (e.g., to store data retrieved from medicaldevice(s) 17) and processing circuitry 98. As described with referenceto FIG. 2, computing device(s) 2 may similarly include a storage deviceand processing circuitry.

Processing circuitry 98 may include one or more processors that areconfigured to implement functionality and/or process instructions forexecution within server(s) 94. For example, processing circuitry 98 maybe capable of processing instructions stored by storage device 96.Processing circuitry 98 may include, for example, microprocessors, DSPs,ASICs, FPGAs, or equivalent integrated or discrete logic circuitry, or acombination of any of the foregoing devices or circuitry. Accordingly,processing circuitry 98 may include any suitable structure, whether inhardware, software, firmware, or any combination thereof, to perform thefunctions ascribed herein to processing circuitry 98. Processingcircuitry 98 of server(s) 94 and/or the processing circuitry ofcomputing device(s) 2 may implement any of the techniques describedherein to analyze physiological parameters received from medicaldevice(s) 17, e.g., to determine a healing progression of patient 4 or ahealth of medical device(s) 17.

Storage device 96 may include a computer-readable storage medium orcomputer-readable storage device. In some examples, storage device 96includes one or more of a short-term memory or a long-term memory.Storage device 96 may include, for example, ROM, RAM, NVRAM, DRAM, SRAM,magnetic discs, optical discs, flash memory, forms of EEPROM or EPROM,or any other digital media. In some examples, storage device 96 is usedto store data indicative of instructions for execution by processingcircuitry 98.

In some examples, one or more of computing device(s) 2 may be a tabletor other smart device located with a clinician (or other HCP), by whichthe clinician may program, receive alerts from, and/or interrogatemedical device(s) 17. For example, the clinician may access datacollected by medical device(s) 17 through computing device 2, such aswhen patient 4 is in between clinician visits to check on a status of amedical condition. In some examples, computing device(s) 2 may transmitdata regarding images, infection or other abnormality indications,training sets, ML models, IMD information, patient identification data,authentication data, etc. to one or more other computing device(s) 2, toedge device(s) 12, and/or to server(s) 94. Likewise, computing device(s)2 may receive similar information.

In further examples, computing device 2 may generate an alert to patient4 (or relay an alert determined by medical device(s) 17, edge device(s)12, or server(s) 94) based on an abnormality determined from acombination of data items, which may enable patient 4 proactively toseek medical attention prior to receiving instructions for a medicalintervention. In the example illustrated by FIG. 3, server(s) 94includes a storage device 96 (e.g., to store data retrieved from medicaldevice(s) 17) and processing circuitry 98. As described with referenceto FIG. 2, computing device(s) 2 may similarly include a storage deviceand processing circuitry.

In some examples, the clinician may enter instructions for a medicalintervention for patient 4 into an application executed by computingdevice 2, such as based on a status of a patient condition determined byanother one of computing device(s) 2, medical device(s) 17, edgedevice(s) 12, server(s) 94, or any combination thereof, or based onother patient data known to the clinician. The patient condition mayinclude an implant status, such as a potential infection at animplantation site of the implant. Computing device 2 of a user mayreceive the instructions, via network 10. In return, computing device 2may display a message on a display device indicating the medicalintervention message.

In some examples, one of computing device(s) 2 may transmit instructionsfor medical intervention to another one of computing device(s) 2 locatedwith patient 4 or a caregiver of patient 4. For example, suchinstructions for medical intervention may include an instruction tochange a drug dosage, timing, or selection, to schedule a visit with theclinician, or to seek medical attention. In this manner, patient 4 maybe empowered to take action, as needed, to address his or her medicalstatus, which may help improve clinical outcomes for patient 4.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of one or more of medical device(s) 17, in accordance withone or more techniques disclosed herein. In the illustrated example,medical device(s) 17 include processing circuitry 40, a storage device50, and communication circuitry 42. Additionally, medical device(s) 17may, in some examples, include one or more electrodes 16, antenna(e) 48,sensing circuitry 52, switching circuitry 58, sensor(s) 62, and powersource 56. As previously stated, medical device(s) 17 may be an exampleof one of medical device(s) 6 of FIG. 1. That is, medical device(s) 17may include an 1 MB, CIED, etc., similar to medical device(s) 6 shownand described with reference to FIG. 1. In another example, medicaldevice(s) 17 may include a non-implanted medical device, such as awearable medical device, a medical workstation cart, etc.

In some examples, one of medical device(s) 17 may be a medical deviceimplanted in patient 4, whereas another one of medical device(s) 17 mayinclude camera 32, and as such, may perform one or more of the varioustechniques of this disclosure. That is, one of medical device(s) 17 may,via camera 32, capture images of a body of patient 4 (e.g., animplantation site of patient 4), in accordance with one or more of thevarious techniques of this disclosure.

Processing circuitry 40 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 40 may includeany one or more of microprocessors, a controllers, DSPs, ASICs, FPGAs,or equivalent discrete or analog logic circuitry. In some examples,processing circuitry 40 may include multiple components, such as anycombination of one or more microprocessors, one or more controllers, oneor more DSPs, one or more ASICs, or one or more FPGAs, as well as otherintegrated or discrete integrated logic circuitry. The functionsattributed to processing circuitry 40 herein may be embodied assoftware, firmware, hardware or any combination thereof.

In some examples, processing circuitry 40 may include AI engine(s) 44and/or ML model(s) 46. AI engine(s) 44 and ML model(s) 46 may be similarto those AI engine(s) and ML model(s) described with reference to FIG.2. In one example, ML model(s) 46 may include one or more DL models thatare trained, for example, on various physiological parameter data, suchas ECG data.

In the illustrated and non-limiting example of FIG. 4, medical device(s)17 includes a plurality of electrodes 16A-16N (collectively, “electrodes16”). Electrode(s) 16 may be referred to in some instances herein as aplurality of “electrode(s) 16,” while in other instances may be referredto simply as “electrode 16,” where appropriate. Electrodes 16 may bedisposed within one bodily layer of patient 4, whereas at least oneother electrode 16 may be disposed within another bodily layer ofpatient 4. In some examples, medical device(s) 17 may sense, viaelectrodes 16, electrical signals attendant to the depolarization andrepolarization of the heart of patient 4.

In some examples, electrodes 16 may be configured for implantationoutside of a thorax of patient 4. In some examples, the housing ofmedical device(s) 17 may be used as an electrode in combination withelectrodes located on leads. In some examples, medical device(s) 17 maybe configured to measure impedance changes within the interstitial fluidof patient 4, ECG morphology changes, etc. For example, medicaldevice(s) 17 may be configured to receive one or more signals indicativeof a subcutaneous tissue impedance. In some examples, computingdevice(s) 2 may utilize such information to determine an abnormality ofIMD 6, such as a device migration abnormality, that computing device(s)2 may use to determine a likelihood of an abnormality (e.g., aninfection) at an implantation site based on images obtained of theimplantation site.

One or more of electrodes 16 may be coupled to at least one lead. Insome examples, medical device(s) 17 may employ electrodes 16 in order toprovide sensing and/or pacing functionalities. The configurations ofelectrodes 16 may be unipolar or bipolar. Sensing circuitry 52 may beselectively coupled to electrodes 16 via switching circuitry 58, e.g.,to select the electrodes 16 and polarity, referred to as the sensingvector, used to sense impedance and/or cardiac signals, as controlled byprocessing circuitry 40. Sensing circuitry 52 may sense signals fromelectrodes 16, e.g., to produce a cardiac EGM or subcutaneous ECG, inorder to facilitate monitoring the post-implant status of IMD 6. Sensingcircuitry 52 also may monitor signals from sensors 54, which may includeone or more accelerometers, pressure sensors, temperature sensors,and/or optical sensors, as examples. In some examples, sensing circuitry52 may include one or more filters and amplifiers for filtering andamplifying signals received from electrodes 16 and/or sensors 54. In anillustrative example, computing device 2 may obtain temperature sensordata from IMD 6 to determine the likelihood of a device pocket infectionin view of images obtained of an implantation site of IMD 6 because atemperature sensor increase at the IMD 6 that occurs prior to atemperature increase at other locations of patient 4 may indicate adevice pocket infection. In some examples, computing device 2 may obtaina thermal image of the implantation site and/or areas adjacent theimplantation site in order to compare the thermal image to thetemperature data and determine the presence of an abnormality at animplantation site, such as where a temperature increase at the IMD 6leads a temperature increase at other exterior locations of patient 4.

In some examples, processing circuitry 40 may use switching circuitry 58to select, e.g., via a data/address bus, which of the availableelectrodes are to be used to obtain various measurements. Switchingcircuitry 58 may include a switch array, switch matrix, multiplexer,transistor array, microelectromechanical switches, or any other type ofswitching device suitable to selectively couple sensing circuitry 58 toselected electrodes. In some examples, sensing circuitry 52 includes oneor more sensing channels, each of which may comprise an amplifier. Inresponse to the signals from processing circuitry 40, switchingcircuitry 58 may couple the outputs from the selected electrodes to oneof the sensing channels.

In some examples, one or more channels of sensing circuitry 52 mayinclude R-wave amplifiers that receive signals from electrodes 16. Insome examples, the R-wave amplifiers may take the form of an automaticgain-controlled amplifier that provides an adjustable sensing thresholdas a function of the measured R-wave amplitude. In addition, in someexamples, one or more channels of sensing circuitry 52 may include aP-wave amplifier that receives signals from electrodes 16. Sensingcircuitry 52 may use the received signals for pacing and sensing in theheart of patient 4. In some examples, the P-wave amplifier may take theform of an automatic gain-controlled amplifier that provides anadjustable sensing threshold as a function of the measured P-waveamplitude. Other amplifiers may also be used. In some examples, sensingcircuitry 52 includes a channel that comprises an amplifier with arelatively wider pass band than the R-wave or P-wave amplifiers. Signalsfrom the selected sensing electrodes that are selected for coupling tothis wide-band amplifier may be provided to a multiplexer, andthereafter converted to multi-bit digital signals by ananalog-to-digital converter (ADC) for storage in storage device 50.Processing circuitry 40 may employ digital signal analysis techniques tocharacterize the digitized signals stored in storage device 50. In someexamples, processing circuitry 40 may detect and classify cardiacarrhythmias from the digitized electrical signals. In some examples,computing device(s) 2 may obtain, via a physiological parametersubsession, physiological parameters (e.g., cardiac arrhythmia data) aspart of a set of data items. In addition, computing device(s) 2 mayobtain, via a device-check subsession, device performance parameters,such as amplifier performance, ADC performance, etc. as part of a set ofdata items comprising interrogation data items. In accordance with oneor more of the various techniques of this disclosure, computingdevice(s) 2 may utilize such data items to elucidate and/or inform ananalysis of images of an implantation site.

In some examples, medical device(s) 17 may include measurement circuitryhaving an amplifier design configured to switch in real-time andcontinuously between multiple and distinct measurement parameters. Inaddition, medical device(s) 17 may enable sensing circuitry 52 and/orswitching circuitry 58 for short periods of time in order to conservepower. In one example, medical device(s) 17 may use an amplifiercircuit, such as a chopper amplifier, according to certain techniquesdescribed in U.S. application Ser. No. 12/872,552 by Denison et al.,entitled “CHOPPER-STABILIZED INSTRUMENTATION AMPLIFIER FOR IMPEDANCEMEASUREMENT,” filed on Aug. 31, 2010, incorporated herein by referencein its entirety.

In some examples, medical device(s) 17 may operate as a therapy deliverydevice. In such examples, medical device(s) 17 may include leads. Theleads may extend to any location within or proximate to a heart or inthe chest of patient 4. In an illustrative and non-limiting example, oneof medical device(s) 17 may include a single lead that extends from oneof medical device(s) 17 into a right atrium or right ventricle, or twoleads that extend into a respective one of a right atrium and a rightventricle.

In some examples, one of medical device(s) 17 may be configured toinclude sensing circuitry, such as sensing circuitry 52, and one or moresensor(s), such as sensor(s) 54. In addition, in some examples, one ofcomputing device(s) 2 may be configured to also include sensingcircuitry, such as sensing circuitry 52, and one or more sensor(s), suchas sensor(s) 54. In one example, one of computing device(s) 2 and/or oneof medical device(s) 17 may include a heart rate sensor, pulse sensor,photoplethysmogram (PPG) sensor, blood oxygen saturation (SpO₂) sensor,etc.

Sensing circuitry 52 may be implemented in one or more processors, suchas in one or more processors of processing circuitry 40 of medicaldevice(s) 17 or processing circuitry 20 of computing device(s) 2.Sensing circuitry 52 is, in the example of FIG. 4, shown in conjunctionwith sensor(s) 54. Similar to processing circuitry 20, 98, 40, 64 andother circuitry described herein, sensing circuitry 52 may be embodiedas one or more hardware modules, software modules, firmware modules, orany combination thereof.

In some examples, at least one of medical device(s) 17 may include asensor device, such as an activity sensor, heart rate sensor, a wearabledevice worn by patient 4, a temperature sensor, a chemical sensor, animpedance sensor, etc. The one or more other medical device(s) 17 may,in some examples, be an external device that is external to patient 4relative to a body of patient 4 or relative to medical device(s) 17implanted in patient 4. In any case, medical device(s) 17 may interfacewith one another via communication circuitry 42, and may, in someexamples, interface, in a similar fashion, with computing device(s) 2,edge device(s) 12, etc. In an illustrative and non-limiting example,computing device(s) 2 may obtain temperature sensor data obtained fromtemperature sensors on-board IMD 6 and may correlate such data withimage data in order to predict a potential abnormality. In an example,computing device(s) 2 may correlate images with increase bodytemperature, either of patient 4 or IMD 6 in order to determine thepresence of, for example, an infection at the implantation site. Inanother example, computing device(s) 2 may obtain chemistry data fromthe chemical sensor that may be indicative of lactate build and pHchanges, which are leading indicators for abnormalities, such asinfections, at an implantation site of IMD 6. Computing device(s) 2 maycorrelate such data in view of baseline values obtained, for example,prior to implantation of IMD 6. In an example, impedance monitoring maybe useful in detecting device migration changes, but a number of days,such as 10 days may be useful to wait so as to allow the impedance tosettle following an implantation event. In some examples, computingdevice(s) 2 may determine the impedance has settled to a baseline valueprior to relying on impedance data to determine the presence of apotential abnormality.

In one example, computing device(s) 2 may utilize orientationinformation of the medical device(s) 17, as indicated by theaccelerometer data, in order to adjust image processing parameters, forexample, of computing device(s) 2. In one example, shadows may be formedbased on the orientation of the medical device(s) 17 that affect theimage processing techniques of this disclosure, such that the imageprocessing algorithms may adjust the processing techniques based oninformation about where medical device(s) 17 is situated within patient4. In such instances, computing device(s) 2 may receive information frommedical device(s) 2, such as temperature data and/or orientation data,and utilize such information during an analysis of image data. That is,computing device(s) 2 may analyze the image data received via camera(s)32 in view of information received from medical device(s) 2 in orderaccurately characterize a potential abnormality. In a non-limiting andillustrative example, computing device(s) 2 may be analyzing the imagedata in view of a set of reference images of an implantation siteuploaded immediately following implantation, where the set of referenceimages may no longer align with a positional state of medical device(s)2 due to movement of medical device(s) 2, and thus, computing device(s)2 may adjust the image processing techniques in order to maintain adegree of accuracy while, computing device(s) 2 or another device,ultimately performs the abnormality analysis.

In one example, computing device 2 may determine, from physiologicalparameters obtained via a second subsession, an ECG change thatindicates device migration and as such, increases the likelihood that apotential abnormality is being detected from the image data. In suchinstances, computing device 2 may analyze the image data using a biastoward detecting an abnormality or may include, in a post-implantreport, a heightened likelihood (e.g., probability, confidence interval)that is based on the likelihood of a potential abnormality determinedfrom the first and second set data items.

In another example, computing device 2 may determine an ECG signal andutilize the ECG signal to map to an orientation of IMD 6. In someexamples, computing device 2 may utilize ECG morphology data to map toan orientation of IMD 6 by identifying deflections in an ECG (e.g.,PQRST data) or by comparing the ECG morphology data against apopulation-wide or cohort database. When an ECG morphology indicates ashift from a baseline ECG of patient 4, then computing device(s) 2 mayindicate a device migration abnormality. A device migration abnormalitymay be indicative of a potential infection abnormality. In suchinstances, when computing device(s) 2 is uncertain whether a potentialinfection from a set of images, computing device(s) 2 may bias thedetermination toward an identification of an abnormality in view of theECG morphology data. In another example, computing device 2 may obtain,from one or more of medical device(s) 17 (e.g., IMD 6), lead impedanceinformation. Similar to the above, computing device 2 may utilize IMDinformation (e.g., lead impedance information) as an additional inputfor abnormality detection and/or prediction.

Communication circuitry 42 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as edge device(s) 12, network computing device (e.g.,server(s)), other medical device(s) 17 or sensors, and/or computingdevice(s) 2. Under the control of processing circuitry 40, communicationcircuitry 42 may receive downlink telemetry from, as well as send uplinktelemetry to, edge device(s) 12 or another device with the aid of aninternal or external antenna, e.g., antenna 48. In addition, processingcircuitry 40 may communicate with a network computing device via network10, such as the Medtronic CareLink® Network. Antenna 48 andcommunication circuitry 42 may be configured to transmit and/or receivesignals via inductive coupling, electromagnetic coupling, NFC, RFcommunication, Bluetooth®, Wi-Fi™, or other proprietary ornon-proprietary wireless communication schemes. In one example,processing circuitry 40 may provide data to be uplinked, viacommunication circuitry 42, to edge device(s) 12, computing device(s) 2,and/or other devices, via network 10. In an illustrative example,computing device(s) 2 may receive a signal (e.g., uplink data) from aparticular one of medical device(s) 17 (e.g., IMD 6). In such examples,computing device(s) 2 may determine, from the signal, informationrelevant to patient 4 and/or the particular one of medical device(s) 17(e.g., IMD 6). In one example, the information may include deviceinformation (e.g., IMD information) that corresponds to the particularone of medical device(s) 17 or that corresponds to a set of medicaldevice(s) 17, where the set may include, in addition to one of IMD(s) 6,other paired medical device(s) 17 (e.g., wearable devices, etc.). Insuch examples, computing device(s) 2 may initiate a device interrogationsession in which computing device(s) 2 receive signals from theparticular one of medical device(s) 17 (e.g., IMD 6) indicative ofdevice interrogation data (e.g., battery health, operating parameters,etc.).

In some examples, processing circuitry 40 may provide control signalsusing an address/data bus. In some examples, communication circuitry 42may provide, via a multiplexer, data to processing circuitry 40, wherethe data is received externally via antenna 48. In some examples,medical device(s) 17 may transmit data to another device using a wiredconnection, such as a universal serial bus (USB) connection, ethernetconnection over network 10 (e.g., a LAN), etc.

In some examples, processing circuitry 40 may send temperature data orother device interrogation data to edge device(s) 12 via communicationcircuitry 42. For example, medical device(s) 17 may send internaltemperature measurements to edge device(s) 12. which are then analyzedby edge device(s) 12. In such examples, edge device(s) 12 performs thedescribed processing techniques. Alternatively, medical device(s) 17 mayperform the processing techniques and transmit the abnormality resultsto edge device(s) 12 for reporting purposes, e.g., for providing analert to patient 4 or another user.

In some examples, storage device 50 includes computer-readableinstructions that, when executed by processing circuitry 40, causemedical device(s) 17, including processing circuitry 40, to performvarious functions attributed to medical device(s) 17 and processingcircuitry 40 herein. Storage device 50 may include any volatile,non-volatile, magnetic, optical, or electrical media. For example,storage device 50 may include ROM, RAM, NVRAM, DRAM, SRAM, magneticdiscs, optical discs, flash memory, forms of EEPROM or EPROM, or anyother digital media. Storage device 50 may store, as examples,programmed values for one or more operational parameters of medicaldevice(s) 17 and/or data collected by medical device(s) 17 fortransmission to another device using communication circuitry 42. Datastored by storage device 50 and transmitted by communication circuitry42 to one or more other devices may include electrocardiograms, cardiacEGMs (e.g., digitized EGMs), and/or impedance values, as examples.

The various components of medical device(s) 17 are coupled to powersource 56, which may include a rechargeable or non-rechargeable battery.A non-rechargeable battery may be capable of holding a charge forseveral years, while a rechargeable battery may be charged from anoutlet or other external charging device (e.g., inductive charging). Inexamples involving a rechargeable battery, the rechargeable battery maybe charged on a daily, weekly, or annual basis, for example. In someexamples, power source 56 may be separate from medical device(s) 17 andimplanted in a separate implantation site of patient 4 that may bemonitored for abnormalities, in accordance with one or more of thevarious techniques of this disclosure.

As described herein, medical device(s) 17 may include medical device 6(e.g., IMD 6). In such examples, medical device(s) 17 may have ageometry and size designed for ease of implant and patient comfort.Examples of medical device(s) 17 described in this disclosure may have avolume of 3 cubic centimeters (cm³) or less, 1.5 cm³ or less, or anyvolume therebetween. In addition, medical device(s) 17 may include aproximal end and a distal end that are rounded to reduce discomfort andirritation to surrounding tissue once implanted under the skin ofpatient 4. An example configuration of a medical device 17 is described,as an example, in U.S. Patent Publication No. 2016/0310031, incorporatedherein by reference in its entirety. Computing device(s) 2 may receiveIMD information that include such configuration details of medicaldevice 17 (e.g., IMD size, whether ends are rounded, length of elongatedleads, if any, etc.). As described herein, computing device(s) 2 mayutilize IMD information, such as IMD configuration, to train AIengine(s) 28 and/or ML models(s) 30 to provide an IMD-tailoredabnormality assessment, in accordance with one or more of the varioustechniques of this disclosure.

FIG. 5 is an example UI visualization of an example computing device502, in accordance with one or more techniques of this disclosure.Computing device 502 may be an example of one of computing device(s) 2described with reference to FIG. 1, 2, or 3. Computing device 502 mayinclude one or more cameras 32, in accordance with one or more varioustechniques of this disclosure. As described herein, the one or morecameras 32 may be separate from computing device 502 in some examples.In the illustrative and non-limiting example of FIG. 5 and othersillustrating computing device 502, computing device 502 may include amobile handheld device (e.g., a tablet, smartphone, etc.). Whileillustrated as a mobile handheld device, the techniques of thisdisclosure are not so limited. It will be understood that the UIvisualization elements may be employed using various other devices andin various other settings, such as in a virtual reality, augmentedreality, or mixed reality setting. That is, a user may use cameras 32 ofaugmented reality headsets in order to image an implantation site ofpatient 4, as well as navigate one or more of the UIs of thisdisclosure.

In some examples, a UI visualization or interface, as described herein,may include a UI page or screen of the relevant UI (e.g., UI 22). Insome examples, a user may use navigation buttons (e.g., back, next,scroll buttons, etc.) to navigate laterally (e.g., forward, backward,etc.) through various UI pages or screens. In some instances, the UIvisualization may include a virtual reality and/or augmented realityvisualization, such as when computing device 502 includes a VR, AR, orMR headset. That is, the UI visualization may be presented as animmersive UI program that a user may interact with in a virtual and/oraugmented reality space. A person of skilled in the art will appreciatethat, although shown as different pages of a UI, computing device 502may similarly present, instead of UI pages on a screen of a handheldmobile device, VR UI elements that a user may, nevertheless, navigate inorder to conduct an interactive session in accordance with the one ormore of the various techniques of this disclosure or at least notinconsistent with the one or more of the various techniques of thisdisclosure.

In some examples, computing device 502 may present an interface 504(e.g., via UI 22) that includes various sign-in options. In one example,interface 504 may include a sign in tile 506 or other sign in elements(e.g., camera icon 508). Computing device 502 may receive user input, asthe user input relates to sign in tile 506, in order to invoke and/orinitiate a virtual check-in interactive session, in accordance with oneor more techniques of this disclosure.

In an illustrative example, interface 504 may include a camera icon thatfirst initializes camera 32 for sign in purposes. Computing device 502may receive images from camera 32 and authenticate the user from thereceived images. In some examples, computing device 502 may performfacial recognition or implantation site (e.g., wound) characteristicsrecognition. In some examples, patient 4 may tap computing device 502 tothe implantation site for NFC or RFID authentication. That is, computingdevice 502 may receive NFC or RFID indications. Computing device 502 mayuse such indications to identify and/or authenticate the particularuser. In any case, computing device 502 may load, from storage device24, the interactive session through or based on a reading or scan of abarcode (e.g., a one-dimensional barcode, a two-dimensional barcode,Quick Response (QR) Code™, matrix barcode, etc.). In some instances, thebarcode may be included with a flyer given to patient 4 at implant timeor shortly before/after the implantation procedure.

In some examples, computing device 502 may use one of medical device(s)17 to authenticate user. Medical device(s) 17, in this instance, mayinclude a wearable device that is able to authenticate the user (e.g.,patient 4, HCP, etc.). The wearable device may, in some instances,include a wristband that includes a barcode, RFID chip, etc. In suchinstances, the user could tap medical device 17 to computing device 502or computing device 502 may otherwise scan medical device 17. In someinstances, the tap could be a contactless tap, such as an air tap thatmaintains a small air gap between devices. In any case, in anon-limiting example, computing device 502 may receive theauthenticating information from medical device 17 and authenticate theuser, such as by allowing the user to proceed to a next interface of theinteractive session.

In some examples, UI 22 may include a button 600 (e.g., a soft key, hardkey, etc.) that serves as a sign-in element. That is, button 600 mayinclude a multi-function button that provides various sign-in options.In one example, button 600 may include a fingerprint or other biometricscanner. Button 600 may be on the front, back, or on any other portionof computing device 502.

In some examples, computing device 502 may not present interface 504,such as following a first sign in from a user. In such examples, a usermay select a “remember this device,” “remember me,” and/or “I am theonly relevant user for this device.” Computing device 502 may receivethe user input and forego a sign-in interface for future sign-in events,accordingly. In some instances, a user may want to re-authenticate foreach sign in, such as in cases where multiple IMD patients use the samecomputing device 502 for implantation site or other IMD monitoring.

FIG. 6 is an UI visualization of a launch session interface 602, inaccordance with one or more techniques of this disclosure. In someinstances, the launch session interface 602 may include a launch sessionicon 606 and/or a camera icon 608. Camera icon 608 may be similar tocamera icon 508 except that, once already authenticated, camera icon 608may function as a shortcut to launch a site-check subsession or othersubsession of the virtual check-in process (e.g., the interactivereporting and/or check-in supersession). In such examples, computingdevice 502 may receive user input of camera icon 608, and in turn,computing device 502 may initialize an imaging device (e.g., camera 32)and launch a site-check subsession (e.g., a wound check subsession), asdescribed with reference to FIGS. 15-19.

In some instances, launch session interface 602 may include a profileinterface 604. Profile interface 604, in some examples, may include animage of patient 4, an image of one or more relevant implantation sitesof patient 4, or both. In this way, patient 4 may enjoy an even morepersonalized experience with using the virtual check-in UI. In suchexamples, computing device 502 may access the images from storage device24 or from another storage device, such as via network 10. In someexamples, the images may be images from a previous site-checksubsession.

In some examples, the virtual check in interactive session may be pushedto a device of patient 4, such as through cloud solutions. In suchinstances, computing device 502 may receive a push notification fromedge device 12 or from another device (e.g., server(s) 94 via network10). The push notification may, in some instances, originate fromcomputing device 2 of another user, such as from computing device 2 of aHCP. In some instances, computing device 502 may receive a notificationprompting a virtual check-in session based on a predetermined schedulingreminder. In such instances, an HCP may program a calendar timer that,when expired, causes computing device 502 to provide a push notificationand/or automatically launch the virtual check-in session (e.g., aninteractive reporting session). In some examples, computing device 502may, upon determining a schedule trigger or a push notification (e.g., aHCP push), provide an instruction to a user to open (e.g., launch) avirtual check-in interactive session. In some instances, computingdevice 502 may automatically launch a virtual check-in interactivesession upon determining the schedule trigger or the push notification.That is, computing device 502 may automatically present interface 504 ofFIG. 5, interface 602 of FIG. 6, or in some cases, may, as a default,present interfaces similar to those described with reference to FIGS.7-21. In some instances, computing device 502 may determine whether theuser requires authentication first or not before presenting variousother interfaces.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may be configured to determine identification datafor patient 4. In an example, processing circuitry 20 may determine theidentification data for patient 4. The identification data may includeone or more of: biometric data, RFID data, NFC data, telemetry protocoldata, user login information, authentication data, patient name or username data, etc. In any case, a user may input identification data viacomputing device(s) 2 and/or via camera 32. In one example, computingdevice(s) 2 may include computing device 502 serving as a user check-indevice. In such instances, computing device(s) 2 may determineidentification data for patient 4 via input received via elements ofinterface 504 (e.g., via button 600, where button 600 includes abiometric scanner).

In some examples, patient 4 may provide authenticating data in order togain access to perform the interactive session. The patient 4 mayprovide biometric data in some examples (e.g., facial data, fingerprintdata, iris data, voice data, characteristic implantation site data,etc.). In one example, computing device(s) 2 may include a biometricscanner (e.g., camera 32, fingerprint scanner, etc.) that patient 4 oranother user may use to provide authentication/biometric data. In somecases, camera 32 may image the implantation site to identify patient 4,where processing circuitry 20 includes AI engine(s) 28 and/or MLmodel(s) 30 trained to identify patient 4, to a certain degree ofcertainty, based on one or more initial images of the implantation site.Processing circuitry 20 may identify patient 4 from uniquecharacteristics of the implantation site of patient 4. In anotherexample, processing circuitry 20 may authenticate user based on wirelesscommunication with IMD 6 of patient 4 (e.g., NFC data, telemetryprotocol data, RFID data, etc.).

In some examples, patient 4 may or may not be the main user of computingdevice(s) 2 for purposes of engaging and/or navigating the interactivesession. In an illustrative example, a user, separate from patient 4,may be the user of computing device(s) 2 for purposes of engaging and/ornavigating the interactive session. That is, the user may coordinatewith patient 4 while navigating the interactive session on behalf ofpatient 4. In an illustrative, a user, separate from patient 4, maylogin to access the interactive session. In such instances, the user,upon being authenticated to access the interactive session, may thenidentify patient 4. In some examples, the user may identify the patientby taking a photo of the patient, entering the patients name, scanning abarcode of the patient, imaging the wound site, etc. In an illustrativeexample, the user may take a photo of the face of patient 4 or of theimplantation site. In any case, computing device(s) 2 may perform facialdetection or wound characteristic detection in order to determinepatient 4.

Processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may deploy image processing tools (e.g., AI engine(s) 28and/or ML model(s) 30) in order to perform the authentication process.In an example, processing circuitry 20 may train the image processingtools on patient data, implantation site data, etc. In one example, theimaging processing tools may learn, when scanning an implantation site,for example, how the implantation site is healing over time (e.g., ahealing trend). Generally speaking, characteristics of an implantationsite may change over time and thus, processing circuitry 20 may, overtime, adjust relevant portions of the identification algorithmaccordingly. This is especially useful in instances where theidentification algorithm of processing circuitry 20 utilizes, forexample, image(s) of the implantation site to identify and/orauthenticate a user. In such instances, processing circuitry 20 maystill be able to accurately identify patient 4, even if an amount oftime has passed between check-in sessions.

In some instances, while attempting to identify patient 4 via camera 32,processing circuitry 20 may detect a potential abnormality at thisstage. Computing device(s) 2, in such instances, may request additionalidentification data in order to correctly and/or accurately identify thepatient 4. In some instances, processing circuitry 20 may storeauthentication data (e.g., two-step authentication data) to storagedevice 24 so as to streamline the authentication and identificationprocess for patient 4 in subsequent sessions. In another example,processing circuitry 20 may query a database (e.g., storage device 96 ofremote server(s) 94), via network 10, that maintains patient data (e.g.,usernames, passwords, implantation site characteristic data,implantation site images, etc.). In such instances, processing circuitry20 may identify, from the patient data, patient 4 upon receiving searchresults from the database. In an illustrative example, processingcircuitry 20 may receive user input, via UI 22, indicating a patientname of patient 4. Processing circuitry 20 may query the database andbased on a result of the query, identify patient 4 as a known patient ofsystem 100 (e.g., system 300).

Processing circuitry 20 may reference patient data (e.g., patientidentifiers) such that the same computing device(s) 2 (or the samealgorithm base) can be shared across multiple patients (e.g., in aclinic). As described herein, computing device(s) 2 may adjust, based onpatient data, the base of the site-check algorithm in order to tailorthe process and UI visualizations in order to accommodate eachrespective patient. In another example, a common site-check algorithmmay be deployed to accommodate all patients of a certain class (e.g.,nursing home patients, patients of a particular nursing home, etc.). Inthis way, the site-check algorithms may maintain and provide aparticular level of uniformity for the various users of the interactivesession, where those users may be part of a common class.

In some examples, a plurality of computing device(s) 2, whileconcurrently operating the interactive session, may both capture imagesof the implantation site of a single patient 4 and determine other dataitems (e.g., interrogation data), as well. That is, a computing device 2of patient 4 may, while operating the imaging program, perform thetechniques of this operation, as well as a computing device 2 of acaregiver. In such examples, computing device(s) 2 may synchronize dataand/or analysis results in real-time or in some instances, at a laterpoint in time, such as by waiting until a wireless connection betweencomputing device(s) 2 is available or a network connection becomesavailable (e.g., a connection via network 10). In some instances, untilcomputing device(s) 2 determines to perform a data synchronizationprocess, computing device(s) 2 may store data locally, such as tostorage device 24, or in some instances to storage device 62 of an edgedevice 12.

In some examples, following the receipt of authentication dataidentifying a user of the imaging program, processing circuitry, e.g.,processing circuitry 20 of computing device(s) 2, processing circuitry64 of edge device(s) 12, processing circuitry 98 of server(s) 94, orprocessing circuitry 40 of medical device(s) 17, may determine theidentification data for patient 4. In such examples, the user of theinteractive session may include a HCP, a family member of patient 4,patient 4, etc. To illustrate, processing circuitry 20 may authenticateand authorize the user to access the interactive session. Subsequently,processing circuitry 20 may receive, via UI 22, input data, such as userinput, that effectively identifies patient 4. In one example, the inputdata includes barcode scanning data, selection of patient 4 from adropdown menu (e.g., via a keyword search), manually entered patientinformation, etc. In any event, computing device(s) 2 may thendetermine, from the input, identification data for patient 4, such as bydetermining the name or ID of patient 4.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine IMD information that corresponds toone of medical device(s) 17 (e.g., IMD 6) of patient 4. In an example,processing circuitry 20 may identify, based at least in part on theidentification data, IMD information that corresponds to a particularone of medical device(s) 17. In some examples, the IMD information mayinclude IMD implantation site information, such as where theimplantation site is located on the body of patient 4, a size and shapeof the implantation site, and other information regarding theimplantation site. In another example, IMD information may includehistorical data relating to the implantation site of the IMD and/orhistorical data relating to the 1 MB. In some examples, the historicaldata may include images of the implantation site following theimplantation procedure, wound characteristics, shape and size of theimplantation site (e.g., the wound size), incision information, historyof any complications during surgery, date of the implant, etc. In someexamples, IMD information may further include IMD type information, 1 MBcommunication protocol information, an estimated orientation of medicaldevice(s) 17 (e.g., 1 MB 6), data regarding one or more HCPs (e.g.,surgeons, clinicians) responsible for implanting (e.g., inserting)medical device(s) 17 (e.g., 1 MB 6), information relating to one or moremethods employed by the one or more HCPs when sealing the implantationsite, images of the implantation site over time, etc. Exampleimplantation methods and instruments are described, for example, in U.S.Patent Publication No. 2014/0276928, incorporated herein by reference inits entirety. In any case, computing device(s) 2 may determine thepresence of a potential abnormality at the implantation site (e.g., alikelihood that the potential abnormality is an actual abnormality)based on the implantation methods and instruments used during theimplantation surgery. In one example, computing device(s) 2 may compareimage data of the implantation site against reference image data from alibrary of reference images, where the library may have tagged thereference images with various details such as implantation methods andinstruments (e.g., library metadata). Computing device(s) 2 maydetermine an abnormality at the implantation site by, at least in part,referencing corresponding images from the reference library that includesimilar implantation method and instrument attributes to that of theimplantation site computing device(s) 2 is imaging.

In a non-limiting and illustrative example, computing device 502 maylaunch the interactive session (e.g., a virtual check-in) when promptedby patient 4, when scheduled on a specific date post implant, and/orwhen pushed a request during or around a clinic check. In some examples,computing device 502 may cause an interactive session program for a user(e.g., patient 4) to expire after a given time. In another example, theinteractive session may not include an explicit expiration date (e.g., aso-called “evergreen” application).

In an illustrative example, computing device 502, when providing aninteractive session, may identify a follow-up schedule for the patient.In an example, computing device 502 may receive the follow-up schedule,for example, from one of computing device(s) 2 of an HCP or may accessthe follow-schedule from a database via network 10. The follow-upschedule may define one or more time periods in which computing device502 is configured to prompt the user to conduct the interactive session.In some examples, the one or more time periods may include at least onetime period that corresponds to a predetermined amount of time from adate of implantation of IMD 6. That is, a first time period or at leastone time period of the one or more time periods may be a predeterminedtime from the date of implantation of the IMD (e.g., a number of dayssince implantation of IMD 6).

In an illustrative example, the follow-up schedule may include a firsttime period for computing device 502 to provide a prompt, e.g., 15 daysafter implantation, or after removal, of medical device 17 (e.g., IMD6). In another example, computing device 502 may include a variable timeperiod that an AI engine(s) 28 and/or ML model(s) 30 may determine forpatient 4, such that different check-in schedules may exist fordifferent patients based on various different criteria. In suchexamples, computing device 502 may provide the interactive session inaccordance with the follow-up schedule. In another example, computingdevice 502 may provide a prompt, for example, to a mobile device user,to capture the image data using computing device 502 a predeterminedtime after an implantation resulting in the implant-related wound. Insome examples, computing device 502 identifies the follow-up schedule byreceiving, via communication circuitry 26, a push notification fromanother device, and determining, based on the push notification, a firsttime period for the follow-up schedule. In such examples, the pushnotification may include a HCP push that is received from one ofcomputing device(s) 2 of an HCP, such as an HCP that corresponds topatient 4. In some examples, computing device 502 may identify thefollow-up schedule by receiving a physiological parameter that indicatesan abnormality (e.g., an ECG abnormality) and determining a first timeperiod for the follow-up schedule based on the physiological parameterabnormality. That is, computing device(s) 2 may determine a triggeringevent for identifying a follow-up schedule that includes a trigger basedon an amount of time that has passed, a particular signal received fromone of medical device(s) 17 (e.g., IMD 6), such as an activity level, anECG, etc., or based on a trigger received via network 10 (e.g., acomputing device 2 of an HCP).

FIG. 7 is an UI visualization of a menu interface 702, in accordancewith one or more techniques of this disclosure. Upon initiation of theinteractive session, computing device 502 may output, via UI 22, aninteractive UI, according to the virtual check-in interactive session.In some examples, computing device 502 may provide a top-level interfacethat includes at least one first interface tile. The first interfacetile may correspond to the first subsession. In such instances, thefirst subsession may include a first subinterface level that is at alower level relative to a level of the top-level interface. Thetop-level UI interface may present several tiles from which the user canchoose. In the example, of FIG. 7, the app of this disclosure outputs aUI program that includes one or more graphical UI visualization elements(collectively, UI elements or “tiles”).

While described as being provided as part of a hierarchal structure withtiered-levels, it will be understood that the techniques of thisdisclosure are not so limited, and that the subsession interfaces may beseparate from a first interactive session interface in terms of what auser may see as a default when initialing receiving the sessioninterfaces. In an example involving a mixed reality context, computingdevice 502 may present all interfaces at a single level, where a usermay access each individual subsession interface, for example, by panningthe head of patient 4 around a virtual reality user interface. In anillustrative and non-limiting example, a user may access and/or initiatea subsession from the virtual reality user interface, where the virtualreality interface may, in some instances, convert to an augmentedreality interface, such as when computing device 502 detects selectionof the first subsession (e.g., a site-check subsession). In suchinstances, computing device 502 may transition to an augmented realitymode in which the user may, for example, image an implantation site ofpatient 4 via camera 32 (e.g., a head-worn camera or another cameracommunicatively coupled to computing device 502), in accordance with oneor more of the various techniques of this disclosure. Once the firstsubsession is complete, computing device 502 may revert to a separateuser interface (e.g., the top-level, menu interface or a secondsubsession interface) that is again, in some instances, presented in avirtual reality context. While several examples may be described hereinfor user interaction with the interactive subsession of this disclosure,it will be understood that the interactive subsession and subsession maybe provided in various contexts not necessarily described herein forsake of brevity.

As shown in the illustrative example of FIG. 7, the UI elements ofinterface 702 may include a patient status tile 706, a physiologicalparameters analysis tile 708, a device check tile 710, and/or a sitecheck tile 712. The user (e.g., patient 4, caregiver, physician) canselect any of these tiles (e.g., via a touch input) to leverage thefunctionalities associated with the description of each such tile. Insome examples, the UI elements may comprise interactive graphical unitsthat may be presented to a user via UI 22. In some examples, selectionof the camera icon may cause computing device 502 to automaticallylaunch a site-check subsession so as to provide a shortcut to site-checkpage accessible via site-check tile 712.

In addition, any one of the interfaces described herein may include asession ID tracker tile 704. Session ID tracker tile 704 may providesession and/or subsession tracking information, such as date stamps,timestamps, session ID stamps and/or subsession ID stamps, userinformation, etc. The session ID tracker may further include historicaldata regarding a previous session (e.g., historical data regarding oneor more previous subsessions), such as a summary of the results of aprevious report (e.g., a session report, reports detailing abnormalityresults for one or more subsessions, etc.). In any case, computingdevice 502 may include such session tracking information when generatinga new report for each particular interactive session, each particularsubsession (e.g., sub-reports for subsessions), etc. A top-levelinterface 702 may include multiple session ID tracker tiles 704, such asone for each subsession tile.

In addition, individual interfaces for one or more subsessions mayinclude tracker tiles, that when computing device 502 detects selectionof a particular tracker tile, computing device 502 may retrievehistorical information about each subsession and previous subsessionresults (e.g., reports, etc.). Computing device 502 may provide a pop-upinterface that provides such information via UI 22 or in some instances,may automatically navigate a user, via UI 22, to a report and/or historyinterface for further review. In addition, computing device 502 mayexport such data (e.g., reports, history, etc.) out of the interactivesession interface to another interface and/or to another devicealtogether (e.g., one of computing device(s) 2 of a HCP, one ofserver(s) 94, edge device(s) 12, etc.). In one example, computing device502 may export reports in response to detected selection of an exportreport button (e.g., a soft key), in response to detecting a userselection of one of subsession tracker tiles or interactive sessiontracker tile 704. The exported reports may include multiple reports(e.g., subsession reports) or compilation reports (e.g., interactivesession report) detailing an analysis of a plurality of data items froma plurality of subsessions. In an illustrative example, computing device502 may detect selection of a subsession tracker tile, via a subsessioninterface, and in response, may generate and/or export a reportcorresponding to the particular subsession of a particular interface ofthe interactive session.

In another example, computing device 502 may detect selection of aninteractive-session tracker tile (e.g., tracker tile 704) and inresponse, may generate and/or export a report corresponding to theinteractive session, a report corresponding to the particularsubsessions that have been executed thus far, if not all subsessions,and/or a comprehensive report corresponding to an entire interactivesession, as well as any summary reports for individual subsessions. Inanother example, computing device 502 may generate historical reportsthat include an aggregation of any one or more of the reports, such thatin response to detecting a selection of an interactive-session trackertile or other tracker tile, computing device 502 may retrieve historicalreports and compile and/or summarize reports from the past in order toproduce a single post-implant historical report for export and/ordisplay (e.g., via a pop-up interface). In such instances, computingdevice(s) 2 of an HCP may receive the post-implant report, via network10, such that the HCP may review the historical record and/or a summaryof the historical record for patient 4 (e.g., for medical device(s) 17corresponding to patient 4).

FIG. 8 is a flowchart illustrating an example method of implementingsubsession techniques of a virtual check-in interactive session, inaccordance with one or more techniques of this disclosure. Thesubsession techniques of this disclosure may be used to obtain variousdata items and comprehensively determine abnormalities at an bodily siteof a patient 4 based on a combination of data items.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may obtain image data associated with the body ofpatient 4, and determine a present status of an implant-related wound onthe body of patient 4. In such examples, the image data includes one ormore frames that represent images of a body of patient 4. In addition, aportion of the body of patient 4 includes, in some examples, animplantation site of IMD 6, such that the frames represent images of theimplantation site. In an illustrative example, processing circuitry 20may prompt a mobile device user (e.g., patient 4) to capture the imagedata using the mobile device. In such examples, processing circuitry 20may prompt the mobile device user a predetermined time after animplantation resulting in the implant-related wound. As describedherein, processing circuitry 20 may further obtain data associated withfunctioning of one of medical device(s) 17 implanted within the body ofpatient 4. Processing circuitry 20 may further determine performancemetrics of medical device(s) 17 based on the obtained data (e.g.,interrogation data, diagnostic data). In some examples, processingcircuitry 20 may determine performance metrics of the medical devicebased on the captured image data, such as an image indicative of devicemigration that may, in turn, affect various performance metrics of,e.g., IMD 6.

In an illustrative example, processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of server(s) 94, or processingcircuitry 40 of medical device(s) 17, may monitoring patient 4. In anexample, processing circuitry 20 may provide an interactive sessionconfigured to allow a user to navigate a plurality of subsessionscomprising at least a first subsession and a second subsession that isdistinct from the first subsession, where the first subsession comprisescapturing image data via one or more cameras 32 (802). In someinstances, processing circuitry 20 may provide the interactive sessionin response to being pushed to patient 4 through cloud solutions. In anexample, the interactive session may include a mobile app that is pushedto patient through cloud solutions. In another example, processingcircuitry 20 may provide the interactive session based on a scanning ofa QR code from flyer given at implant time or shortly before/after.

In some instances, processing circuitry, e.g., processing circuitry 20of computing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may provide the interactive session (e.g., a firstsubsession, a site-check subsession, etc.) by training (e.g., viaprocessing circuitry 20) a session generator on one or more of: cohortparameters or IMD information; deploying, via the computing device, thesession generator to generate the interactive session; and providing,via the session generator, the interactive session that, as a result ofthe training, is at least in part personalized for the user. Inaddition, the session generator may reference historical interrogationdata for comparison when personalizing the interactive session. Thesession generator may include AI engine(s) 28 and/or ML model(s) 30.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine a first set of data items inaccordance with the first subsession of the interactive session (804).In an example, processing circuitry 20 may determine a first set of dataitems including image data as part of a site-check subsession.

In some examples, processing circuitry 20 may determine a second set ofdata items in accordance with the second subsession of the interactivesession, the second set of data items distinct from the first set ofdata items and comprising one or more of: data obtained from medicaldevice(s) 17 (e.g., IMD 6), at least one physiological parameter ofpatient 4, or user-input data (806). In some examples, processingcircuitry 20 of computing device(s) 2 or of computing device 502 mayperform a pairing session (e.g., a pairing process) with one or moremedical device(s) 17 (e.g., IMD 6), with another computing device(s) 2,and/or with edge device(s) 12 (e.g., an IoT device in the home). Thepairing session is configured to pair the computing device with therespective device (e.g., IMD 6). In some examples, the pairing sessionmay include an authentication process as described herein. In anillustrative example, a mobile computing device may be initialized tocommunicate with IMD 6, including authentication (e.g., 2-stepauthentication) so that only authorized mobile computing devices caninterface with IMD 6. In such examples, in a first instance, IMD 6 maytransmit a signal to computing device 2 in response to computing deviceattempting to pair with IMD 6.

The user may then authenticate themselves, and if proper, IMD 6 may thenpair with computing device 2 and initiate a bidirectional orunidirectional communication between devices. IMD 6 may then remember aunique ID of the computing device 2 for future authentication (e.g., viaa hash key, neural network, etc.). In any case, computing device 2 mayreceive during the pairing session information about the 1 MB. In suchinstances, computing device 2 may receive, via the pairing process, theinterrogation data regarding 1 MB 6 (e.g., from 1 MB 6). In someexamples, computing device 2 may store the interrogation data ashistorical interrogation data for subsequent reference and/or to use astraining sets for AI engine(s) 28 and/or ML model(s) 30. That is, 1 MBinformation, in some instances, may include device interrogation data(e.g., historical interrogation data).

In accordance with one or more of the various techniques of thisdisclosure, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine, based at least in part on the firstset of data items and the second set of data items, an abnormalitycorresponding to at least one of patient 4 and/or IMD 6 (808). In anexample, processing circuitry 20 may output a post-implant report of theinteractive session, wherein the post-implant report includes indicationof the abnormality (810). Processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of server(s) 94, or processingcircuitry 40 of medical device(s) 17, may provide patient 4 with thecapability to generate a report (e.g., in PDF format or in various otherformats) for their records, to email or FTP to family members or to adoctor, etc. In some examples, the post-implant report may furtherinclude indication of an amount of time that has transpired since thedate of implantation of 1 MB 6. In some examples, processing circuitry20 may determine the post-implant report by determining, from the secondset of data items, the abnormality (e.g., an ECG abnormality) anddetermining, based at least in part on the abnormality and the images,the post-implant report. In any case, processing circuitry 20 maydetermine a manner in which to provide feedback to patient 4, such asthrough graphical simplified icons or complex results (e.g., dependingon HCP preference). At the completion of the interactive session,processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may, in some instances, mark the results on UI 22 providedvia the mobile device app, with date and time stamping.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may output the post-implant report by outputtingthe post-implant report to a device of a HCP via network 10 (e.g., via abidirectional communication protocol). In one example, processingcircuitry 20 may output, via network 10, the post-implant report toanother device of the user of UI 22 and/or the interactive session,where the user may or may not necessarily be patient 4.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine that the interactive session iscomplete and provide a notification that the interactive session iscomplete, such as by providing a confirmation receipt or by markingtiles of a UI 22 interface as having a threshold number of subsessionscomplete. In some examples, processing circuitry 20 may output a resultof the post-implant report for display (via a display of computingdevice(s) 2 or of another device (e.g., one of medical device(s) 17 thatincludes a display). In an illustrative example, when processingcircuitry 20 determines that any one or any combination of subsessionsyielded an abnormal result (or abnormal outside an acceptable margin ofnormal), processing circuitry 20 may use the mobile device app to outputa prompt to patient 4. In some examples, the prompt may indicate anabnormality. In other examples, the prompt may include a recommendationor instruction to schedule a follow-up visit with the HCP.

In some examples, the first set of data items may include an imageoverlay (e.g., an augmented reality overlay). The image overlay may beused to augment a set of preview frames for particular angle and sizereference. In such examples, processing circuitry 20 may output theimage overlay at the first subinterface level. In addition, whenproviding the first subsession, processing circuitry 20 may detectselection of a first interface tile, and provide the first subsession ofthe interactive session. In another example, processing circuitry 20,when determining the first set of data items, may provide a prompt forthe user (e.g., patient 4, an HCP, etc.) to utilize the one or morecameras 32 to capture the image data, and determine, subsequent to theprompt, at least a portion of the first set of data items (e.g., theimage data or at least a portion of the image data). In any case,processing circuitry 20 may prompt a mobile device user to capture theimage data using the mobile device a predetermined time after animplantation resulting in the implant-related wound.

In some examples, processing circuitry 20 may provide, via a secondsubsession interface, a second subsession of the interactive session,and in turn, may modify the second interface tile to indicate completionof the second subsession (e.g., with a checkmark). In addition,processing circuitry 20 may provide, subsequent to the secondsubsession, the first subsession of the interactive session in order toobtain image data and may, in turn, modify the first interface tile toindicate completion of the first subsession. In some examples, for aconvenience of user navigation, processing circuitry 20 may facilitatenavigation from the second subsession interface directly to the firstsubsession interface without necessarily involving a top-levelinterface. In any case, processing circuitry 20 may also provide,subsequent to the first subsession or the second subsession, a thirdsubsession of the interactive session, and determine a third set of dataitems in accordance with the third subsession of the interactivesession, where the third set of data items is distinct from the firstset of data items. In such examples, determining the third set of dataitems may include processing circuitry 20 receiving physiologicalparameter signals and determining, from the physiological parametersignals, the third set of data items. It should be noted that thesubsession described in this disclosure may be provided in any order,accessed in any order, and in some instances, processing circuitry 20may phase out one or more subsessions at particular periods of time,such as a predetermined time from implantation, a predetermined timefrom a user completing a particular interactive session, a result of aparticular interactive session (e.g., such as an implantation sitestatus), etc. In one example, processing circuitry 20 may disable accessto an imaging subsession after a predetermined time from implantation orafter a predetermined time from a healthy status update (e.g., a woundbeing continuously projected to heal according to an expected schedule).In such examples, processing circuitry 20 may generate the interactivesession to include one of a remaining set of subsession to include withthe interactive session at a prospective time. In some examples,processing circuitry 20 may generate the interactive session to includea disabled or phased out subsession (e.g., the site-check subsession)such that the disabled subsession is hidden or otherwise inaccessible tothe user. Processing circuitry 20 may provide the interactive session toinclude the subsessions, even if some are rendered inaccessible to theuser.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may identify an abnormality from the variousdata items. In one examples, processing circuitry 20 may determine theabnormality to include a first abnormality. In such examples, processingcircuitry 20 may determine the post-implant report by outputting, viacommunication circuitry 26, the second set of data items to another oneof computing device(s) 2. In turn, processing circuitry 20 may receive,via communication circuitry 26, a result of an analysis of any one ormore of the sets of data items (e.g., the second set of data items). Theresult may indicate that the second set of data items does not indicatethe presence of a second abnormality. In such examples, processingcircuitry 20 may determine the post-implant report based at least inpart on the first abnormality and the second set of data items.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may provide the interactive session, including athird subsession and a fourth subsession. In an example, processingcircuitry 20 may provide a third subsession that includes aphysiological-parameter subsession and may provide a fourth subsessionthat includes a device-check subsession. In such instances, processingcircuitry 20 may determine the abnormality by determining, in accordancewith the third subsession, a third set of data items, where the thirdset of data items includes at least one physiological parameter of thepatient and determining, in accordance with the fourth subsession, afourth set of data items, where the fourth set of data items includesinterrogation data. As such, processing circuitry 20 may furtherdetermine the abnormality based at least in part on the third set ofdata items and/or the fourth set of data items.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may initiate the second subsession subsequent tothe first subsession. In an example processing circuitry 20 maydetermine a first set of data items by receiving, via communicationcircuitry 26, the first set of data items, and may determine the secondset of data items by receiving, via the computing device(s) 2, thesecond set of data items. In some examples, processing circuitry 20 maydetermine a second set of data items that comprises informationindicative of an abnormality of the at least one physiological parameterof the patient or an abnormality of a device parameter corresponding toone or more medical device(s) 17. In such examples, processing circuitry20 may when determining an indication of the abnormality, output, to anabnormality determiner for an abnormality analysis, at least one of thefirst set of data items or the second set of data items, and determine aresult of the abnormality analysis, where the result indicates theabnormality. In such instances, the abnormality determiner may includeat least one of AI engine(s) 28 and/or ML model(s) 30. In one example,one of computing device(s) 2 may include and deploy the abnormalitydeterminer to determine an abnormality at a bodily site of patient 4.That is, processing circuitry 20 may deploy the abnormality determinerto determine the abnormality, where the abnormality determiner may betrained, as described herein, to identify various abnormalities based onimage data and on other data items.

In some examples, processing circuitry 20 may determine, as thepotential abnormality, the presence of a potential infection at theimplantation site. That is, processing circuitry 20 may, in identifyingthe abnormality at the implantation site, determine the presence of apotential infection at the implantation site. In some examplesprocessing circuitry 20 may detect the potential abnormality at theimplantation site based on an analysis of the at least one frame. Inanother example, processing circuitry 20 may determine the potentialabnormality by transmitting, via communication circuitry 26, an imageand other data items to edge device(s) 12. That is, computing device(s)2 may transmit one or more frames of image data to edge device(s) 12,where the frame(s) include one or more images of the implantation site.In addition, computing device(s) 2 may transmit the image and/or otherdata items, via network 10, to edge device(s) 12, medical device(s) 17(e.g., a wearable device, a bedside workstation), and/or server(s) 94.

In some examples, computing device(s) 2 may transmit the image and/orother data items to another device, such as server(s) 94, via network10, in which case, server(s) 94 may transmit the image and/or other dataitems to edge device(s) 12 for further analysis. That is, in someexamples, computing device(s) 2 may transmit data, such as image orvideo data, indirectly to edge device(s) 12 and/or server(s) 94, vianetwork 10. In one example, computing device(s) 2 may transmit data toedge device(s) 12, which, in turn, performs processing of the dataand/or transmission of the data (e.g., edge-processed data) to server(s)94 for further analysis. In such instances, edge device(s) 12 and/orserver(s) 94 may determine the presence of a potential abnormality basedon data (e.g., image data, video data, etc.) received from computingdevice(s) 2 via communication circuitry 26. In any case, computingdevice(s) 2 may determine the presence of a potential abnormality at theimplantation site upon receiving the potential abnormality informationfrom another device (e.g., edge device(s) 12, server(s) 94, etc.).

In an illustrative example, edge device(s) 12 and/or server(s) 94 mayreceive the image and/or other data items (e.g., second set of dataitems, third set of data items, fourth set of data items, etc.) fromcomputing device(s) 2. In some instances, edge device(s) 12 and/orserver(s) 94 may perform an image processing analysis prior totransmitting a result of the image processing analysis to computingdevice(s) 2 and/or edge device(s) 12. In some examples, edge device(s)12 may identify the presence of the potential abnormality based on ananalysis of the image data and/or the other data items of the othersubsessions. In some examples, edge device(s) 12 may deploy imageprocessing engine(s) (e.g., via AI engine(s) 28 and/or ML model(s) 30)to determine the presence of the potential abnormality. In anotherexample, edge device(s) 12 may perform some or all of the analysis withassistance from server(s) 94. That is, in some examples, server(s) 94 oredge device(s) 12 may include image processing engines or variousanalysis tools, configured to assist in the detection of a potentialabnormality. Server(s) 94 and/or edge device(s) 12 may include imageprocessing engines, such as AI engine(s) 28 or ML model(s) 30, describedwith reference to FIG. 2. In one example, server(s) 94 or edge device(s)12 may include training sets for training one or more imaging processingengine(s). Server(s) 94 and/or edge device(s) 12 may perform thetraining of the image processing engine(s) or in some instances, mayassist computing device(s) 2 with training the image processingengine(s). In another example, server(s) and/or edge device(s) 12 maytransmit training sets to computing device(s) 2. In such instances,computing device(s) 2 may train the image processing algorithms (e.g.,via AI engine(s) 28 and/or ML models(s) 30). In any case, computingdevice(s) 2 may determine, from the images, the presence of a potentialinfection at the implantation site based on an analysis of the images.

In addition, when identifying the abnormality, processing circuitry,e.g., processing circuitry 20 of computing device(s) 2, processingcircuitry 64 of edge device(s) 12, processing circuitry 98 of server(s)94, or processing circuitry 40 of medical device(s) 17, may determinethe likelihood that the potential abnormality is an actual abnormality(e.g., a severity metric, probability metric, etc.). In one example,processing circuitry 20 may determine the likelihood that the potentialabnormality is an actual abnormality based on information that definesthe potential abnormality (e.g., class of abnormality, abnormalitycharacteristics, IMD type, etc.). In some instances, processingcircuitry 20 may determine the likelihood by receiving, from anotherdevice (e.g., edge device(s) 12, server(s) 94, etc.), data indicating alikelihood of the potential abnormality being an actual abnormality orin some instances, data indicating a likelihood that the potentialabnormality poses a health risk, whether serious or not. In anon-limiting example, processing circuitry 20 may determine thelikelihood that a potential infection determined from a set of imagesrepresents an actual infection.

As a person skilled in the art would appreciate, an actual infection(e.g., a real infection) may be evidenced by a confirmed or verifiedpresence of an infectious agent at the implantation site. In otherwords, a potential infection determination is an unverifieddetermination that an implantation site has become infected with aninfectious agent. That is, computing device(s) 2 may determine apotential infection as a possible infection based on data available tocomputing device(s) 2, but computing device(s) 2 may not diagnose anactual infection without additional input indicating that the infectionis, in fact, an actual infection. In any case, computing device(s) 2 maydetermine, based at least in part on the images and one or more otherdata items, a likelihood that the potential infection at theimplantation site is an actual infection. In addition, an actualabnormality, such as a healing abnormality, may be evidenced by anactual abnormality in the healing process, such as may be confirmed orverified by another source (e.g., an HCP).

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may use weighting factors (e.g., a built-in bias,error margins, etc.) when determining the likelihood of the potentialabnormality being an actual abnormality. In one example, processingcircuitry 20 may train AI engine(s) 28 and/or ML models(s) 30 on a poolof data, including patient data, implantation site data, rate of falsepositives, error margin information, etc., in order to determine anaccurate likelihood of the potential abnormality being an actualabnormality. In one example, a built-in bias may include a bias towardconfirming a potential abnormality as being more likely than not anactual abnormality when the IMD is of a particular type or when aparticular number of images indicate a potential abnormality.

FIG. 9 is an UI visualization of a patient status interface 902 thatpresents upon user request of patient status interface illustrated inFIG. 7. That is, FIG. 9 illustrates a UI visualization screenshots ofinvocation and initiation of UIs of the user-facing mobile device app ofthis disclosure upon the user selection of the patient status tileillustrated in FIG. 1. Various UI visualizations of this disclosure mayinclude navigation buttons 906A-906N. The navigation buttons may includea back button 906A, a home button 906B, a next button 906C, an endbutton 906N, etc. In some instances, button 600 may function as a homebutton 906B that returns a user to a home of the computing device 502 ora home page of the application, such as interface 602 or 702. Interface902 may include a start session tile 904, which when selected, maylaunch a patient status subsession configured to elicit or solicitpatient input. In some examples, patient status interface 902 may serveas a hub for health information for patient 4. In one example, inaddition to health information received via computing device 502,computing device 502 may also receive health information from otherdevices, wearable devices, or other software applications.

FIG. 10 is a UI visualization of an example patient status interface1002, in accordance with one or more techniques of this disclosure. FIG.10 illustrates a non-limiting example of a patient status questionnairethat either the backend system or the mobile device application of thisdisclosure may generate. The patient status questionnaire may be used togauge information on a general health picture of patient 4, onimplant-recovery-specific symptoms, medications that the patient hastaken recently or will take soon, etc. Patient status interface 1002 maybe a UI page that allows patient 4 or another user to enter informationabout patient 4, for example, via free text, dropdown menus, radiobuttons, etc. In some examples, computing device(s) 2 may train AIengine(s) 28 and/or ML model(s) 30 on patient health information inorder to more accurately determine abnormality information for animplantation site (e.g., severity of abnormality type, etc.). In oneexample, computing device(s) 2 may receive user input indicatingsoreness, redness, etc. at the implantation site. In such instances, AIengine(s) 28 and/or ML model(s) 30 may utilize such information whendetermining a potential abnormality. In some instances, computing device502 may receive the patient health information as audio data, in whichcase, computing device 502 may train AI engine(s) 28 and/or ML model(s)30 on the audio data.

As described herein, in some examples, the second subsession may includethe patient-status subsession. In such examples, processing circuitry 20may, via UI 22, determine a set of data items according to thepatient-status subsession. In an example, processing circuitry 20 mayreceive user-input data in accordance with the second subsession of theinteractive session and determine, from the user-input data, a secondset of data items. The user-input data may include one or more of:patient-entered data, medication information, symptom information,physiological metrics, or anatomical metrics. In an illustrativeexample, the user-input data may include patient pain levels, sorenesslevels, perceptions of redness at or adjacent the implantation site,etc. Processing circuitry 20 may utilize such information in order todetermine, from image data, whether an abnormality is present at oradjacent an implantation site. In one example, processing circuitry 20may receive an image of a user pointing at particular area of a bodilysite of patient 4 and may indicate, via user-input, that the indicatedarea is sore at a particular soreness level. In some examples,processing circuitry 20 may, in accordance with a site-check subsession,obtain one or more additional images of the bodily site and performimage processing and analysis in view of the user-input and the gestureindication (e.g., pointing) in order to determine with particularity andconfidence at a particular confidence interval that a specific area ofthe bodily site includes a potential abnormality. In some instances,processing circuitry 20 may indicate the potential abnormality at adifferent area of the bodily site (e.g., above the implantation site),even where the patient indicates another area is sore (e.g., below theimplantation site), where processing circuitry 20 identifies from theimage data and/or other data items that the potential abnormality ismore likely linked to one area (e.g., above the implantation site) overanother area (e.g., below the implantation site).

In some examples, processing circuitry 20 may input the user-input datainto a risk calculator (e.g., AI engine(s) 28 and/or ML model(s) 30)that is configured to control the frequency at which patient 4 receivessubsequent notifications to image a particular bodily site of patient 4.In an example, when a symptom risk score is high, processing circuitry20 may prompt patient 4 more often to image a bodily site of patient 4.

FIG. 11 is a UI visualization of an example physiological parametercheck interface 1102, in accordance with one or more techniques of thisdisclosure. The physiological parameter check interface 1102 may includebuttons including, start physiological parameters analysis 1104,previous parameter 1106, next parameter 1108, and a physiologicalparameters menu. In some cases, physiological parameter check interface1102 illustrates the interface that is invoked or initialized upon theuser selecting the physiological parameters analysis tile illustrated inFIG. 7 (e.g., tile 708).

FIG. 12 is a UI visualization of an example physiological parametercheck interface 1202, in accordance with one or more techniques of thisdisclosure. In an illustrative example, physiological parameter checkinterface 1202 includes an ECG analysis page. Physiological parametercheck interface 1202 illustrates, as a non-limiting example, twooutcomes, namely, a normal ECG analysis 1204 and an abnormal ECGanalysis 1208 (e.g., as indicated by an abnormality 1210 observed in theECG). The ECG may be received from one of medical device(s) 6, medicaldevice(s) 17 (e.g., an IMD), or another device such as a watch, fitnesstracker, or other wearable device configured to collect ECG data. In anycase, computing device(s) 2 may obtain ECG data for analysis. In anon-limiting and illustrative example, the analysis may include a normalresult 1206 or abnormal result 1212, in which case an alert to contact aclinic may be warranted.

In some examples, a subsession may include the physiological-parametersubsession. In such examples, processing circuitry 20 may determine aset of data items in accordance with the physiological-parametersubsession. In an example, processing circuitry 20 may receive, inaccordance with the second subsession, at least one physiologicalparameter corresponding to patient 4, and determine, from the at leastone physiological parameter, a set of data items. In some examples,processing circuitry 20 may receive, via communication circuitry 26, theat least one physiological parameter from one or more medical device(s)17, including IMD 6. The at least one physiological parameter comprisesat least one of: an electrocardiogram (ECG) parameter, a respirationparameter, an impedance parameter, a core body temperature, a skinsurface temperature, an activity parameter, blood pressure, vitals,glucose levels, rhythm data, and/or a pressure parameter. In someexamples, the ECG parameter represents an abnormal ECG. In suchinstances, processing circuitry 20 may determine an abnormality at abodily site of patient 4 based at least in part on the abnormal ECG, andin some instances, coupled with image data. The physiological parametersmay include health signals retrieved from IMD 6 or from one or moreother medical device(s) 17, such as another IMD or a wearable devicecomprising one or more sensors.

Examples of ECG collection aspects include ECG collected directly fromone of medical device(s) 17 (e.g., medical device(s) 6). In someexamples, medical device(s) 17 may include a wearable device, such as anactivity tracker, heart rate monitor, pulse monitor, pulse oximetrymonitor, temperature monitor (e.g., core temperature monitor, surfacetemperature monitor). In addition, computing device(s) 2 may receive ECGthat is collected from wearables or other ECG devices (e.g., medicaldevice(s) 17). In some examples, computing device(s) 2 may provide forprogrammed connectivity (e.g., via a dropdown menu) with medicaldevice(s) 17. Computing device(s) 2 may receive input, via the dropdownmenu, from patient 4 or HCP indicating a target programming connectionfor one or more of medical device(s) 17 (e.g., a wireless connection).In some examples, the virtual check-in application of this disclosureincludes a device-aware application. That is, computing device(s) 2 maystore information regarding what device computing device(s) 2 iscommunicating with. In some instances, computing device(s) 2 maydetermine such information through a pairing process. In anotherexample, computing device(s) 2 may receive such information as adownload from a physician. In some examples, computing device(s) 2 maydetermine such information from user selection from a dropdown menu(e.g., a device dropdown menu).

In some examples, computing device(s) 2 may include device-aware aspectsthat are based on an interrogation of one of medical device(s) 17. Inanother example, computing device(s) 2 may receive device parameters andinformation as pushed from another device, such as from a pushnotification. In some examples, computing device(s) 2 may receive deviceinformation from another one of computing device(s) 2 operated by a HCP,where the HCP may populate the correct information via UI 22.

In some instances, computing device(s) 2 may receive physiologicalparameters or physiological parameter analysis results directly fromanother device or indirectly from another device, such as over network10. In an illustrative example, computing device may receive an ECG oran image of an ECG from another device. That is, a scanner program mayscan an ECG and upload the scan in any suitable document format.Likewise, a loader program may upload an image of an ECG for use bycomputing device(s) 2. In some instances, computing device(s) 2 mayinclude the scanner program and/or the loader program. In suchinstances, computing device(s) 2 may upload the physiological parameterinformation to another device or store the parameter information to aninternal storage (e.g., storage device 24).

In some examples, circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of data server(s) 94, or processing circuitry 40 of medicaldevice(s) 17 may, prior to providing the second subsession, detectselection of a second interface tile, wherein a first interface tilecorresponding to a first subsession and the second interface tilecorresponding to a second subsession are distinct from one another. Inan example, processing circuitry 20 may provide, in response toselection of the second interface tile, the second subsession of theinteractive session.

In some examples, the top-level interface of computing device 502 mayinclude the second interface tile. In another example, an interface forthe first subsession may include the second interface tile, such that auser may navigate from the first subsession to the second subsessionwithout necessarily reverting to the top-level interface.

FIG. 13 is a UI visualization of an example device check interface 1302,in accordance with one or more techniques of this disclosure. Devicecheck interface 1302 illustrates an example interface that may beinvoked or initialized upon user selection of the device check tileillustrated in FIG. 7 (e.g., tile 710).

FIG. 14 is a UI visualization of an example device check interface 1402,in accordance with one or more techniques of this disclosure. Invocationof device check interface 1402 may cause computing device(s) 2 tointerrogate medical device(s) 17 (e.g., IMD 6). In an example, computingdevice(s) 2 may interrogate medical device(s) 17 via various close-rangewireless communication protocols and telemetries. Computing device(s) 2may populate device check interface 1402 with various parameters for thedevice check portion, as shown. In various examples, the backend systemof a cloud-based implementation may push the interrogation informationto the mobile device, or the mobile device app may initiate theinterrogation locally at the mobile device. In any case, users may thenaudit the performance of medical device(s) 17 (e.g., IMD 6), by invokingthe app and pinging the statistics as shown in FIG. 14. In the exampleof FIG. 14, the monitored statistics include, but are not necessarilylimited to, battery strength, impedance, pulse width, pacing percentage,pulse amplitude, and pacing mode. In some instances, computing device(s)2 may automatically schedule a routine device-check or follow-up basedon the results of the interrogation.

In such examples, the subsession of interface 1302 and 1402 includes adevice-check subsession, where processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of server(s) 94, or processingcircuitry 40 of medical device(s) 17, may determine the second set ofdata items. In an example, processing circuitry 20 may perform, viacommunication circuitry 26, an interrogation of one or more of medicaldevice(s) 17 corresponding to the patient. The one or more medicaldevice(s) 17 may include IMD 6, and in some instances, other medicaldevice(s) 17, such as wearable monitoring devices. Processing circuitry20 may perform the interrogation by establishing a wirelesscommunication connection with the respective medical device(s) 17. Thatis, processing circuitry 20 may receive, via a computing network, thesecond set of data items. In some examples, the computing device mayreceive medical device interrogation data over network 10, directly fromthe one or more medical device(s) 17, from one or more edge device(s)12, or any combination thereof. In any case, processing circuitry 20 maydetermine, from the interrogation, the second set of data items, wherethe second set of data items include device interrogation data (e.g.,battery, impedance, pulse width, etc.). In some examples, processingcircuitry 20 may determine the post-implant report upon completion ofthe device-check subsession. That is, processing circuitry 20 maydetermine, from the set of data items obtained via the device-checksubsession, that medical device(s) 17 satisfy one or more performancethresholds. In one example, processing circuitry 20 may compareinterrogation data to what was read during implantation. In suchexamples, processing circuitry 20 may determine, based at least in parton the data items of the device-check subsession, a post-implant report.Examples of post-implant reports, and generation of such reports, aredescribed herein (e.g., with reference to FIGS. 21-23).

FIG. 15 is a UI visualization of an example site check interface 1502,in accordance with one or more techniques of this disclosure. Site checkinterface 1502 illustrates the invocation or initiation of a site-checksubsession upon the user selection of the site check tile illustrated inFIG. 7 (e.g., tile 712).

Site check interface 1502 may include a start check 1504 button and acamera icon 608 button. Camera icon 608 may automatically start asite-check subsession. In some examples, however, a user may wish toadjust camera parameters prior to starting. Although not illustrated,site check interface 1502 may include additional options to adjustvarious camera parameters. In addition, a user may adjust cameraparameters on the fly while using the camera to capture images of theimplantation site. In some examples, camera parameters include adjustingbetween a front-facing camera and another camera, lighting (e.g.,infrared, thermal imaging, flash, etc.), zoom levels, focus, contrast,etc. Once ready, processing circuitry 20 may receive user selection, viaUI 22, of start check 1504 in order to advance to a next page (e.g.,interface) of the site-check subsession (e.g., a next graphicalinterface of the site-check UIs).

FIG. 16 is a flowchart illustrating an example method of utilizing imageacquisition, recognition, and/or image processing techniques, inaccordance with one or more techniques of this disclosure. Describedwith reference to the interfaces of FIGS. 15, 17, and 18, for example,processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may receive a UI command to start the site-checksubsession (e.g., a wound check subsession). In one example, processingcircuitry 20 may receive indication of user input selecting start checkicon 1504.

In an illustrative example, a method for imaging an implantation site isdescribed (e.g., an image capture process). Camera 32 captures image(s)of or adjacent the implantation site of one of medical device(s) 17(e.g., IMD(s) 6 (1612). Processing circuitry 20 may capture images at aspecific point in time following implantation of the medical device 17(e.g., IMD 6). In some examples, a first one of computing device(s) 2may store the various images to storage device 24 (1614). In addition,or alternatively, computing device(s) 2 may transmit the various imagesto a secure backend system. Example backend systems include edgedevice(s) 12, server(s) 94, and/or other components of network 10 (e.g.,Medtronic CareLink® Network). Processing circuitry of the backend system(e.g., processing circuitry 64, processing circuitry 98, etc.) mayprocess the various images and/or route the images to various otherdevices. Processing circuitry 20 may, in some examples, store image(s)to storage device 50 of medical device(s) 17. Similarly, processingcircuitry 20 may store such data to storage device 62 of edge device(s)12 or to storage device 96 of server(s) 94.

Processing circuitry 20 may guide the user by providing scheduledreminders. The reminders may be pushed to a computing device 2 of theuser. In addition, a HCP (e.g., the prescribing physician) can accessthe interactive session interface(s) (e.g., site-check subsessioninterface) to follow-up on patient 4 when the implantation site does notshow signs of healing over time. That is, processing circuitry 20 maydetermine an abnormality, such as an implantation site that does notdisplay signs of healing over time, and as such, may transmit anotification to a computing device 2 of the HCP, such that the HCP mayaccess the interactive session interface(s) (e.g., site-check subsessioninterface), including images, physiological parameters, etc., via UI 22.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may deploy, as part of the site-check subsession,a ML model (e.g., a DL model) and/or AI engine that analyzes, forabnormalities, each captured image from a site-check subsession. In anillustrative and non-limiting example, the site-check subsession mayprovide three levels of detections: “abnormality present,” “abnormalityabsent,” “uncertain.” In an example, where processing circuitry 20, viathe site-check subsession, detects an abnormality in any of the imageswith a high likelihood, then processing circuitry 20 may determine anabnormality presence at that time. Where processing circuitry 20determines there to be no abnormality in all images with a highlikelihood, then processing circuitry 20 may determine an abnormalityabsence at that time. In some examples, processing circuitry 20 maytransmit images from the site-check subsession to another one ofcomputing device(s) 2 (e.g., a computing device 2 of a technician orHCP) for human expert review. The “expert” can be a trained professionalwho can review images of the body of patient 4 and identify somepotential abnormalities and/or can capture images of a patient'simplantation site with relative clarity. If the human expert detects anabnormality, the patient can be asked to follow-up with the prescribingphysician.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine thresholds for abnormality presenceor absence. In some examples, processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of server(s) 94, or processingcircuitry 40 of medical device(s) 17, may determine parameters of thesite-check subsession, including thresholds for abnormality detection,based on based on whether the implantation site hosts a first-timeimplant or an implant that has been changed out, IMD type (e.g., CIEDtype), skin tone and age of patient 4 being monitored, etc.

In an illustrative example, processing circuitry 20 may determine asensitivity level for the site-check subsession (e.g., abnormalitydetection algorithms, etc.). To determine the sensitivity level,processing circuitry 20 may determine medical device information (e.g.,IMD information) with respect to medical device(s) 17, physiologicalparameters (e.g., ECG), etc. In such examples, processing circuitry 20may determine the sensitivity level based on the determined medicaldevice information, physiological parameters, etc. In an illustrativeand non-limiting example, processing circuitry 20 may determine aprevalence factor that defines the prevalence of abnormalities (e.g.,infections) for a particular type of IMD. In another example, processingcircuitry 20 may determine an impact factor that defines the impact anabnormality (e.g., device malfunction, infection, etc.) may have withrespect to patient 4 and medical device(s) 17 (e.g., IMD 6). Processingcircuitry 20 may determine, from such information or other information,a sensitivity level that yields a more conservative algorithm that errson the side of over detection, rather than under detection, ofabnormalities. That is, it may be important to be more conservative andnot miss a potential abnormality (e.g., infection, malfunction of anIMD, etc.) because doing so may then provide high-energy life-savingtherapy. In one example, IMD 6 may malfunction to a point where it isunable to determine temperature or migration data of IMB 6, such thatwhen a potential abnormality is detected from an image, processingcircuitry 20 may determine not to rely on data received from IMB 6 inorder to identify, according to an adjusted sensitivity level, alikelihood of the abnormality being an actual abnormality.

In one further example, processing circuitry 20 may determine a higher(e.g., more conservative) sensitivity level relative to other lessconservative sensitivity levels, when processing circuitry 20determines, for example, that a stimulation generator (not shown) andleads (not shown) are from different manufacturers but are beingcombined into a single IMB 6. That is, the likelihood of a potentialabnormality may be higher in such instances where different items arebeing combined from different manufacturers into a single device, andthus, processing circuitry 20 may adjust the sensitivity level so as tobe a higher sensitivity level in such instances. In some examples,processing circuitry 20 may adjust the sensitivity level (e.g., abuilt-in calculation bias) for identifying particular abnormalitiesbased on a duration of time IMB 6 has been implanted. This is becausecertain abnormalities (e.g., pocket infections) may be most commonduring, for example, the first year post-implant, and as such, a highersensitivity level may be utilized during the particular timeframe (e.g.,the first year) and/or a lower sensitivity level may be utilized duringanother time frame (e.g., after the first year).

In another example, medical device(s) 17 (e.g., IMB 6) may include aLINQ™ ICM. In such instances, processing circuitry 20 may employ adetection algorithm that has a different sensitivity level for the LINQ™ICM compared to that of a sensitivity level used for other medicaldevice(s) 17 (e.g., a pacemaker implant). That is, the detectionalgorithm may employ a particular sensitivity level where the medicaldevice information indicates that the medical device was implanted aspart of a particular type of procedure (e.g., an out-patient procedure).In addition, processing circuitry 20 may determine from the IMDinformation that a schedule for virtual check-ins, or other types ofcheck-ins, are scheduled at a particular interval (e.g., regularintervals, irregular intervals, frequent intervals, infrequentintervals, etc.). In another example, processing circuitry 20 maydetermine a frequency by which the virtual monitoring service isreceiving and/or monitoring medical device diagnostics, such asdiagnostics from medical device(s) 17 (e.g., IMD 6, wearable heart rateand/or activity monitor, etc.).

In an illustrative and non-limiting example, processing circuitry, e.g.,processing circuitry 20 of computing device(s) 2, processing circuitry64 of edge device(s) 12, processing circuitry 98 of server(s) 94, orprocessing circuitry 40 of medical device(s) 17, may employ a firstsensitivity level when determining an abnormality for a first type ofimplant (e.g., a pacemaker implant), whereas the processing circuitrymay employ a second sensitivity level when determining an abnormalityfor a second type of implant (e.g., an ICM). In an example, the firstsensitivity level may be higher than the second sensitivity levelbecause processing circuitry 20 may have determined that the implantprocedure of a second type of implant was out-of-office or out-patient,that there are one or more regularly-scheduled patient follow-ups viasystem 100 and/or system 300, and that processing circuitry 20 iscontinuously, or at least semi-continuously, monitoring diagnosticdevice data of a medical device, such as would results from regular datatransmissions from medical device(s) 17 to computing device(s) 2 and/orto other devices (e.g., edge device(s) 12, etc.) of system 300.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine parameters of an imaging program ofcamera 32, including thresholds for abnormality detection, based onwhether the implantation site hosts a first-time implant or an implantthat has been changed out, IMD type (e.g., CIED type), skin tone and ageof patient 4 being monitored, etc. In some examples, processingcircuitry, e.g., processing circuitry 20 of computing device(s) 2,processing circuitry 64 of edge device(s) 12, processing circuitry 98 ofserver(s) 94, or processing circuitry 40 of medical device(s) 17, maydetermine parameters of the imaging program based on informationregarding various abnormality control procedures. In an illustrativeexamples, an abnormality control procedure may include information thata HCP (e.g., a surgeon or implanting clinician) used Medtronic's TYRX™Absorbable Antibacterial Envelope, or another similar element, duringimplantation of the medical device. As will be understood, TYRX™ is amesh envelope that holds an implantable cardiac device, implantableneurostimulator, or other IMD. Processing circuitry 20 may determine abiasing factor for the based on the presence of such control procedures.This is because TYRX™ is designed to stabilize the device afterimplantation while releasing antimicrobial agents, minocycline andrifampin, over a minimum of seven days, and thus, the likelihood of anabnormality during that time is lower relative to an implant that doesnot include such control procedures. In other words, patients with aTYRX™ envelope as part of an implant generally tend to have a lowerchance of infection than patients without a TYRX™ envelope. In any case,the various abnormality detection algorithms of this disclosure may beconfigured to be more sensitive and/or less specific (e.g., imagingprogram parameters) for non-TYRX™ patients as compared to imagingprogram parameters used with respect to TYRX™ patients.

In some examples, AI engine(s) and/or ML model(s), e.g., AI engine(s) 28of computing device(s) 2, AI engine(s) 44 of medical device(s) 17, MLmodel(s) 30 of computing device(s) 2, or ML model(s) 46 of medicaldevice(s) 17, may determine the sensitivity level based on training ofthe AI engine(s) and/or ML model(s). In an example, in order todetermine sensitivity levels, processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of server(s) 94, or processingcircuitry 40 of medical device(s) 17, may train the AI engine(s) and/orML model(s) on data sets, including prevalence data (e.g., rate ofinfection with particular types of medical device(s) 17), severity data,data on likelihood of abnormalities, potential or actual impacts ofparticular types of abnormality (e.g., device malfunction and infectionabnormalities, etc.), IMD information (e.g., device manufacturinginformation, implantation procedure information), etc. Upon beingtrained on such data, the AI engine(s) and/or ML model(s) may determinea sensitivity level that corresponds to each individual monitoring event(e.g., device contexts) or class of monitoring events of patient 4.

Advantages of applying such sensitivity levels include allowingmonitoring system 100 to efficiently allocate processing, memory, and/orpower resources, by scaling the sensitivity level according toindividual needs. In addition, the sensitivity level may govern a rateat which computing device(s) 2 receive data from other devices (e.g.,transmission rates, etc.). In this way, computing device(s) 2 mayreceive more data, for example, in situations involving a higherlikelihood of an abnormality being present, and receive less data inother situations. This selective use of sensitivity levels may alsoassist with bandwidth considerations, such as by limiting an amount ofcommunication data that may otherwise consume a large amount ofbandwidth of system 100 and/or system 300.

In some examples, the site-check subsession, including AI engine(s) 28and/or ML models(s) 30 may be trained on several images/videos that havebeen labeled as corresponding to an abnormality or not (e.g., infectionor not). In one example, a video may provide evidence of a gait ofpatient 4. In such examples, processing circuitry 20 may include asite-check subsession executed by AI engine(s) 28 and/or ML model(s) 30.AI engine(s) 28 and/or ML model(s) 30 may be trained with data that hasbeen labeled based on improvement of an implantation site ordeterioration of the implantation site. In an example, AI engine(s) 28and/or ML model(s) 30 can detect, as an implant status, implantationsite “improvement” or implantation site “non-improvement” over time.

In some examples, processing circuitry 20 may acquire images ofimplantation site from multiple site-check subsessions over time afterthe implantation. Processing circuitry 20, via the site-checksubsession, may chronologically analyze the time-aging of theimplantation site with different thresholds to detect improvement ornon-improvement of the implantation site. The prescribing physician canuse the system to only follow-up on patients whose implantation sitedoes not show signs of healing. In some examples, processing circuitry20 may operate according to a time-lapse mode. During time-lapse,processing circuitry 20 may, for example, collect images chronologically(e.g., on a daily schedule) and stitch them together over the 2-12 weeksperiod post implant. This will allow for a time-sequence presentation ofthe images and time-sequence analysis. In such examples, processingcircuitry 20 may train AI engine(s) 28 and/or ML model(s) 30 on a rateof change of implantation site healing (e.g., infection spread orgrowth). In some examples, rather than assess the implantation site perse, AI engine(s) 28 and/or ML model(s) 30 may instead analyze theprogression of the implantation site healing (e.g., a delta betweenregistered and superimposed successive images that track differencesbetween the images over a multiweek period). In some instances, AIengine(s) 28 and/or ML model(s) 30 may track differences betweenstill-images in order to determine a delta at the implantation site overtime (e.g., a delta between healing of the implantation site in atime-lapse). Processing circuitry 20 may then determine abnormalitiesbased on an analysis of the delta at the implantation site over time.

In some examples, processing circuitry 20 may determine, based on theimages, the presence of a potential abnormality (1616). In one example,the backend system may route the various images to a computing device 2of an expert technician trained in identifying abnormalities from imagesof implantation sites. In another example, the backend system may routethe various images to a second one of computing device(s) 2 that has aparticular configuration of AI engine(s) 28 and/or ML model(s) 46trained in identifying abnormalities from implantation-site images. Insome examples, when identifying the abnormality, processing circuitry 20may determine, based on an image of the implantation site (e.g., thecaptured image(s) of the implantation site), a likelihood of theabnormality (e.g., a severity of the abnormality). In some instances,processing circuitry 20 may deploy a probability model to determine thelikelihood of the abnormality. In one example, processing circuitry 20may determine a potential severity of the potential abnormality based onan analysis of the captured image(s).

In some examples, processing circuitry 20 may output a summary of theimplant status, including potential abnormality information (1618). Inone example, the backend system may transmit a report back to the firstone of computing device(s) 2 indicating a result of the image analysisperformed by the backend system. In an illustrative example, processingcircuitry 20 may generate, based on the likelihood of the abnormality, asummary report identifying the abnormality, where the summary mayinclude information regarding a determined potential infection. In someexamples, processing circuitry 20 may store all image data and labels(e.g., expert labels) in a database and computation system tocontinuously improve and deploy the automated abnormality detectionsystem (e.g., AI engine(s) 28 and/or ML model(s) 30). In any case,processing circuitry 20 may output the summary report. In some examples,the summary report includes the one or more frames of the image data. Insuch examples, processing circuitry 20 may generate a summary report(e.g., a post-implant report) including the one or more frames of theimage data, where that image data was received prior to the abnormalitydetermination and used at least in part to determine the abnormalitydetermination. In some instances, the report includes the frames thatcorrespond to one or more camera angle(s) in which the potentialabnormality was identified (e.g., left lateral vs. right lateral). Insome examples, when an abnormality is detected, processing circuitry 20may output one or more various image(s) of the implantation site fromthat subsession for human expert review.

In some examples, computing device(s) 2 may determine, from theinteractive session, whether to configure the interactive session toinclude a pass-thru mode, where patient 4 does not need to come in for aconsultation (e.g., for a post-implantation infection consultation). Insuch examples, computing device(s) 2 may provide the HCP with access tothe various images (e.g., still-images). As such, the HCP may, in someexamples, decide whether the implantation site is healing as expected orwhether an in-person follow-up is warranted.

In some examples, a HCP may have programmed and transmitted theinteractive session program to an application database (e.g., a programdatastore). In another example, a HCP may have uploaded the interactivesession program directly to computing device(s) 2. That is, computingdevice(s) 2 may access the interactive session program from anapplication database. In an example, the interactive session program mayinclude an imaging program for conducting a site-check subsession. Thatis, the imaging program may be part of the interactive session program,whereas in some instances, the imaging program that executes thesite-check subsession may be separate from the interactive sessionprogram. In addition, some aspects of the site-check subsession programmay be controlled by a separate programming application, such as acamera application native to the operating software of one of computingdevice(s) 2 (e.g., an OEM camera application), while other aspects maybe controlled by the interactive session programming application, suchas augmented overlays, camera parameter control (e.g., zoom, contrast,focus, etc.), such that the interactive session software may work intandem with other software applications (e.g., camera applications,augmented reality applications, etc.) or devices running such softwareapplications thereon (e.g., augmented reality headsets, etc.).

The HCP may configure the interactive session to function in thepass-thru mode where the patient may forego the post-implantconsultation entirely until execution of the interactive session resultsin an identified potential abnormality, such as one that satisfies apredefined threshold. In such examples, the HCP may, in any case, accessthe images in order to determine whether an abnormality is present,regardless of whether processing circuitry 20 determines the presence ofa potential abnormality. In this way, the HCP (e.g., a physician, nurse,etc.) may independently adjudicate the image(s) independent of anautomated image analysis of the site-check subsession, in order toindependently determine a status of the implant, including animplantation site healing status.

FIG. 17 is a UI visualization of an example site check interface 1702,in accordance with one or more techniques of this disclosure. Site checkinterface 1702 includes a photographs icon 608 configured to causecamera 32 to capture images of implantation site 1704.

In some instances, a user may point the camera at implantation site1704, in which case, processing circuitry 20 may automatically performan abnormality detection, with or without already receiving a command tocapture an image. In addition, processing circuitry 20 may cause camera32 to automatically capture an image of the implantation site whenprocessing circuitry 20 detects an abnormality. Once a photo has beencaptured, an implant status indicator 1706 may appear indicating astatus of the implant, including the implantation site 1704. In theexample of FIG. 17, the implantation site is indicated as being“Normal”. In some examples, processing circuitry 20 may perform such adetection by comparing a first image (automatically or manuallycaptured) with one or more baseline images (e.g., a first set of imagescaptured during a predetermined period of time as measured fromimplantation or as measured from when the first image is taken). In oneexample, processing circuitry 20 may apply a sliding window to a set ofhistorical images taken over time to filter images (e.g., older images)from the one or more baseline images and compare the first image withthe one or more filtered baseline images. In addition, processingcircuitry 20 may determine a projection from the one or more filteredbaseline images and determine, at a prospective time, a reference (e.g.,reference alteration characteristics) that the first image may becompared against to determine the presence of an abnormality at theimaged site.

In some examples, processing circuitry 20 may implement regionalcomparison zones for differential diagnostics (e.g., Dx). Processingcircuitry 20 may perform a gradient analysis of implantation site 1704and, for example, sternum areas for different skin tones, to determine adifferential diagnostic. Processing circuitry 20 may reference thedifferential diagnostic to determine whether a potential abnormality ispresent at implantation site 1704. AI engine(s) 28 and/or ML model(s) 30also use various measurements in the user-provided picture to determinewhether the implantation site is within a threshold of the “normal”state. In some instances, processing circuitry 20 may overlay a ruler orother augment or overlay on the image to assist the user with capturingan image useful for measurements. That is, processing circuitry 20 mayprovide an augment or another frame that the user can use to get thecorrect distance and/or perspective of the implantation site. In anillustrative example, processing circuitry 20 may guide a user to imagethe implantation site where camera 32 is a target distance from theimplantation site and in some instances, from a second target distancefrom the implantation site. Processing circuitry 20 may further guide auser to image the implantation site at a target angle relative to theimplantation site or relative to a reference plane of camera 32 (e.g., astarting position of computing device(s) 2). In such examples,processing circuitry 20 may use overlays, augmented rulers, etc., suchas in an augmented reality implementation, in order to capture image(s)at particular angles (e.g., a particular view of the implantation site),with the implantation site at a particular relative size in a frame ofimage data, etc.

In some examples, processing circuitry 20 may train AI engine(s) 28and/or ML model(s) 30 to discern relative measurements with respect tocolor and marks. In this way, processing circuitry 20 may compensate fordifferent appearances of a wound on different skin colors, acrossdifferent patient demographic cohorts, etc. In some examples, the imageprocessing AI may use regional comparisons within the same picture todistinguish the implantation site from areas of the skin of patient 4that are unaffected by the implantation (e.g., to derive relative ordelta information). The image processing AI may also be trained tocompensate for differences between systemic infections and implantsite-originated infections, such as at the pocket or incision. In someexamples, processing circuitry 20 may deploy AI engine(s) 28 and/or MLmodel(s) 30 to determine, from a captured image, Red, Green, and Blue(RGB) image details, Pantone®, and other color schemes. In addition, AIengine(s) 28 and/or ML model(s) 30 to determine, from a captured image,shape of an implantation site wound closure, whether the image comprisesglossiness, etc. Processing circuitry 20 may, based on the various imageprocessing techniques, determine whether an image of an implantationsite includes a potential abnormality or if the implantation siteotherwise satisfies the predefined threshold of the “normal” state.

FIG. 18 is a UI visualization of an example site check interface 1802,in accordance with one or more techniques of this disclosure. Theexample site check interface 1802 of FIG. 18 illustrates an abnormalimplantation site outcome. When detecting an abnormality processingcircuitry 20 may provide a visual alert 1806. Processing circuitry mayinclude in visual alert 1806 descriptive information regarding thepotential abnormality. In some examples, processing circuitry mayinclude with visual alert 1806 a medical intervention instruction forthe user to perform some action (e.g., contact clinic, etc.).

In some examples, processing circuitry 20 may provide augmented realityoverlay 1804 on interface 1802 (e.g., an image overlay). Processingcircuitry 20 may do so in order to assist a user in capturing an imageof the implantation site at a particular angle, with a size reference,etc. In addition, AI engine(s) 28 and/or ML model(s) 30 may provideadjustments for a respective algorithm based on a tendency that, as analteration of the implantation site occurs (e.g., a gradual healing, agradual decline in healing, etc.), the post-op images will change aswell. In some examples, processing circuitry 20 may train AI engine(s)28 and/or ML model(s) 30 to detect deviations from the “healthy” state,in some examples. The healthy state may include characteristics of theimplantation site from a previous site-check subsession in which noabnormality was detected.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may identify an image overlay to augment a set ofpreview frames and guide a user in capturing images in a particularmanner. In one example, processing circuitry 20 may determine patientdata corresponding to the patient. The processing circuitry 20 may, inturn, determine the image overlay data based at least in part on thepatient data. As discussed herein, patient data includes variousinformation about the IMD patient including any one or more of: imagedata of an implantation site (e.g., an explant site), implantation sitecharacteristics, patient identification data (e.g., patient name,authentication data, login information, etc.), patient-input data (e.g.,input via UI 22), or other information regarding IMD 6, including dateof implant or explant, IMD component details (e.g., leads, wiringroutes, etc.). The image data may include a current image or apreviously taken image, such as one taken shortly after implantation orbefore an implantation procedure. In such examples, the image overlaydata may correspond to the IMD and/or other components of the IMD. Asused herein, preview frames generally refer to the frames of image datadisplayed to a user prior to image capture and/or during an imagecapture. The preview frames represent what a user observes on a displayscreen of, for example, computing device(s) 2. In any case, the imageoverlay data defines an image overlay configured to augment the set ofpreview frames. In some instances, the image overlay may include awireframe that resembles an outline of a body of a subject, such aspatient 4 or a generic silhouette.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may obtain images of the body of the patient,wherein the images correspond to the image overlay and represent one ormore locations of the body in which one or more components of the IMDcoincide (e.g., IMD 6, leads of IMD 6). In one example, the images mayrepresent a right or left pectoral region of the body, where IMD 6comprises an implant configured to be implanted in a pectoral region ofthe body. In another example, the images may represent a portion of theneck of patient 4, where the user is tasked with imaging portions of theneck where leads of IMD 6 coincide, such as leads routed from IMD 6 to aregion of the brain of patient 4. From the images, processing circuitry20 may determine an abnormality (e.g., an infection, discharge, etc.) atthe one or more locations of the body in which one or more components ofthe IMD coincide.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine the image overlay data to include ahollow outline of a body with an outline of a region of interest (e.g.,an implantation site), such that the user may align the image properlyto capture the region of interest at particular angles, sizes,orientations, etc. In an example, processing circuitry 20 may determinethe image overlay data to include a first portion comprising an outlineof at least one portion of the body of patient 4, where the portion ofthe body corresponds to an implantation site of the one or more IMDcomponents, and determine a second portion of the image overlay data,where the second portion comprises an inner overlay that is inside ofthe outline and that represents the region of interest (e.g., animplantation site).

In an illustrative and non-limiting example, processing circuitry, e.g.,processing circuitry 20 of computing device(s) 2, processing circuitry64 of edge device(s) 12, processing circuitry 98 of server(s) 94, orprocessing circuitry 40 of medical device(s) 17, may determine, based atleast in part on the patient data, that the IMD coincides with aparticular lateral side of a pectoral region of the body. In an example,processing circuitry 20 may determine the first portion of the imageoverlay data so as to represent the particular lateral side of thepectoral region of the body. In some examples, processing circuitry 20may determine the second portion of the image overlay. In such examples,processing circuitry 20 may determine, based at least in part on thepatient data, that implantation site 1704 comprises a particularincision angle relative to the outline of the at least one portion ofthe body of patient 4. That is, the image overlay data may be dynamicand based on the particular context of the imaging of patient 4, such asbased on the type of medical device 17, location of medical device 17,etc. In any case, processing circuitry 20 may determine the secondportion so as to represent the implantation site comprising theparticular incision angle.

In addition, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may obtain one or more images of the body inaccordance with the image overlay. In an example, processing circuitry20 may obtain the images of the body by determining, from a set ofpreview frames, that the second portion of the image overlay coincideswith the implantation site of the one or more IMD components. Inresponse to determining the overlay, processing circuitry 20 initiate anautomatic capture of the images of the body of patient 4. As usedherein, “automatically” or “automatic” generally means without userintervention or control.

In another example, processing circuitry, e.g., processing circuitry 20of computing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine the image overlay data from alibrary of image overlays. In an example, processing circuitry 20 maydetermine a static image overlay configured to provide a border foraligning the implantation site within the static image overlay. In anillustrative example, the border may include a broken-lined box shape,similar to overlay 1804 shown in FIG. 18. In such examples, processingcircuitry 20 may retrieve the static image overlay from an overlaylibrary that includes the at least one image overlay. In some examples,processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may determine, based at least in part on patient data, IMDinformation, and/or a template overlay (e.g., a static image overlay), acustom image overlay as the image overlay that is customized for patient4. In an illustrative example, processing circuitry 20 may obtain theimages of the body by detecting movement of computing device 2 (e.g.,via accelerometer data), and compensating for the movement bymaintaining a particular orientation of the image overlay relative tothe set of preview frames and the one or more locations of the body ofpatient 4.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine the image overlay data by receiving,via camera processor(s) 38, a subset of the preview frames, anddetermining the image overlay data based on the subset of the previewframes. In such examples, AI engine(s) 28 and/or ML model(s) 30 maydetermine an initial estimation as to what the scar/implantation sitelooks like and what patient's body looks like (shoulder frame, build,etc.) based on patient data, IMD information, etc. Then, as camera 32 ispointing at the implantation site, AI engine(s) 28 and/or ML model(s) 30may fine-tune, or morph in real-time, the image overlay to better framethe wound site with reference to other characteristics of the body ofpatient 4. In this way, the user may be guided by the image overlay toaccurately align the implantation site within the overlay outline insuch a way that is then useful for computing device 2 to analyze imagecaptures of the implantation site and know how the wound site is angledon the body, size of the wound site, etc., in accordance with one ormore of the various techniques of this disclosure. In addition, theimage overlay data may be used to train the AI to determine imageoverlays for future imaging sessions. In another example, processingcircuitry 20 may use a template overlay as an initial guess, or may usean already-modified template, and then the template may be modified inreal-time once actual image data is being received by camera processor38. In such examples, processing circuitry 20 may customize the imageoverlay to fit or mold to the build/frame of the body of patient 4 inaddition to representing other characteristics that may be present inthe image of the implantation site (e.g., collar bone for chest implant,hairline for DBS implants, etc.).

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine the image overlay data based on adetermination as to a particular camera configuration of camera 32. Insuch examples, processing circuitry 20 may determine whether afront-facing or rear-facing camera of a mobile computing device is beingused to capture images of the body of patient 4 or whether camera 32 isa standalone camera unit that a user may stand near such that camera 32may capture images of the body of patient 4. In an illustrative example,processing circuitry 20 may require respective mirror images for theimage overlay when the camera being used is a rear facing camera of amobile computing device, as opposed to a front facing camera of themobile computing device. In addition, camera configurations may includenumber of cameras (e.g., dual camera, triple camera) and types of lenses(wide angle, 360° lens, etc.), which would require different imageoverlays depending on the camera being used. In such examples,processing circuitry 20 may determine the image overlay data based atleast in part on the camera configuration. Processing circuitry 20 mayautomatically capture an image of the implantation site when the imageoverlay defined by the image overlay data overlaps with the implantationsite or other bodily site of patient 4 that is of interest. As usedherein, an “overlap” may not include a perfect overlap, but may involveat least a substantial overlap, such that a particular percentage of thebodily site overlaps with the image overlay. In an example, when 80-90%of an implantation site is within the boundary of a particular portionof an image overlay, processing circuitry 20 may determine that anoverlap exists, such that the image overlay portion substantiallyoverlaps with the bodily site.

In such examples, the image overlay data may be configured to guide animage capture of the body of patient 4 relative to particular distancesfrom the one or more computing devices to a surface of the body. In someexamples, processing circuitry 20 may determine the image overlay databy creating, via an overlay generator (e.g., AI engine(s) 28 and/or MLmodel(s) 30, a wireframe (e.g., a custom wireframe, a templatewireframe, etc.), where the overlay generator is trained to generatewireframes based on patient data. A wireframe generally refers to anaugment that includes an outline, such as a hollow outline, that is usedto align an object in a scene being captured within the wireframe. Theuser may adequately perceive the object in the scene through a wireframein the foreground and perceive the wireframe in the foreground, as well,so as to augment a frame of image data as would be appreciated in theart. In some examples, obtaining the images of the body of patient 4,may include receiving, via communication circuitry 26, one or moreframes of image data, wherein the one or more frames include the imagesof the body of patient 4. In another example, obtaining the images ofthe body of patient 4 includes capturing video data of the one or morelocations of the body of the patient (e.g., with assistance from theimage overlay). In such instances, processing circuitry 20 may determinethe abnormality comprises identifying the abnormality from the videodata and determine a post-implant report based on identification of theabnormality.

FIG. 19 is a flowchart illustrating an example method of capturing animage of a body, in accordance with one or more techniques of thisdisclosure. In some examples, processing circuitry, e.g., processingcircuitry 20 of computing device(s) 2, processing circuitry 64 of edgedevice(s) 12, processing circuitry 98 of data server(s) 94, orprocessing circuitry 40 of medical device(s) 17 may create apersonalized baseline for patient 4 and allow AI engine(s) 28 (e.g.,inference engines) and/or ML model(s) 30 to provide trend-basedanalysis. In an example, processing circuitry 20 may deploy AI engine(s)28 (e.g., inference engines) and/or ML model(s) 30 configured toevaluate images based on historical frames of image data to fit a trendline, rather than evaluating images based on a snapshot (e.g., singlestill-image) or even based on successive snapshots evaluating againstone another rather than being evaluated against baseline characteristicsof patient 4. In any case, such an evaluation may inform patient 4and/or an HCP about how an implantation site (e.g., an explant site) isaltering (e.g., progressing) over time towards healing or an abnormalitysituation (e.g., a rapidly worsening infection or a slowly developinginfection). In an example, processing circuitry 20 may enable apreprocessing algorithm to automatically adjust captured images to scaleand orientation, so that users reviewing a time lapse of images get aconsistent view while reviewing successive images over time. Inaddition, processing circuitry 20 may enable higher predictionconfidence in the image analysis due to the presence of contextual dataavailable at the point of inference, such as with an inference AI enginereviewing images of a particular site-check subsession, in addition toreviewing other data items of the interactive session, to determine acomprehensive post-implant report.

In some examples, AI engine(s) 28 and/or ML models(s) 30 may be trainedon several images that have been labeled as corresponding to anabnormality or not (e.g., infection or not). In such examples, AIengine(s) 28 and/or ML model(s) 30 may be trained with data that hasbeen labeled based on improvement of an implantation site. In anexample, AI engine(s) 28 and/or ML model(s) 30 can detect, as an implantstatus, implantation site “improvement” or implantation site“non-improvement” over time.

In some examples, processing circuitry 20 may acquire images ofimplantation site from multiple interactive sessions over time.Processing circuitry 20 may maintain a chronology of images to detectimprovement or non-improvement of the implantation site. In one example,processing circuitry 20 may collect images on a predetermined scheduleand align the images over a period of time post-implant. In suchexamples, processing circuitry 20 may train AI engine(s) 28 and/or MLmodel(s) 30 on how much the implantation site characteristics alter overtime. In some examples, AI engine(s) 28 and/or ML model(s) 30 maydetermine a post-implant report based on changes between successiveimages that identifies differences between the images over time.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may identify a first set of images thatrepresent a particular location of the body of patient 4 (1902). In anexample, processing circuitry 20 may identify a first set of images thatrepresent a particular location of the body in which at least onecomponent of IMD 6 coincides (e.g., IMD 6, lead wires, etc.). In someexamples, the first set of images may include a single image, while inother examples, the first set of images may include multiple images.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may determine a projection of alterationcharacteristics, such as healing characteristics (1904). In an example,processing circuitry 20 may determine a trend line or model thatrepresents the projection of alteration characteristics over time. Thatis, the projection generally represents alteration characteristics ofthe particular location of the body of patient 4 projected over time.The particular location of the body may include the implantation site ofIMD 6 or may include other bodily sites of patient 4 that coincide withother components of IMD 6. In some examples, processing circuitry 20 maydetermine the projection of alteration characteristics based on imagesof other IMD recipients, such as recipients that align with a particularcohort of patient 4.

In an illustrative example, processing circuitry 20 may determine theprojection of alteration characteristics from the first set of images.In such examples, processing circuitry 20 may determine a commonalignment reference for aligning the first set of images in a time-lapserepresentation of the first set of images. In some examples, processingcircuitry 20 may align, according to the common alignment reference, aplurality of images from the first set of images to determine thetime-lapse representation. In some examples, processing circuitry 20 maydetermine, from the plurality of aligned images, the projection ofalteration characteristics. In some examples, each image of the firstset of images may be validated to determine whether the image should beincluded in the plurality of images. In one example, processingcircuitry 20 may validate images for quality (e.g., blurriness),orientation (e.g., portrait only), and may discard images that fail thevalidation test.

As described herein, the particular location of the body may, in variousexamples, include an implantation site of the at least one component ofIMD 6. In such examples, the common alignment reference includes analignment truth that utilizes one or more of: a relative angle of theimplantation site, a relative size of the implantation site, todetermine and implement the alignment truth to, for example, align theimages in a time lapse configuration. In some examples, the commonalignment reference may further include: image lighting characteristics,skin pigmentation characteristics, a relative orientation of theimplantation site, or a relative location of the implantation site inrespective frames of image data representing the first set of images.

In an illustrative example, processing circuitry 20 may align theplurality of images from the first set of images. In an example,processing circuitry 20 may provide, during image capture, an imageoverlay configured to augment a set of preview frames. Processingcircuitry 20 may determine that an image of the particular location ofthe body from the set of preview frames overlaps with the image overlay.Processing circuitry 20 may obtain, based at least in part on theoverlap, a first image of the first set of images, where the first imagerepresents the particular location of the body in accordance with thecommon alignment reference. Processing circuitry 20 may align, via theplurality of images, images of the implantation site with the commonalignment reference.

In some examples, processing circuitry 20 may determine a set ofpre-implant images representing the particular location of the bodyprior to implantation. In an example, the set of pre-implant images mayinclude images captured prior to an implantation procedure, such thatthe pre-implant images represent a baseline for patient 4 that, afterhealing, patient 4 may return to with the, at times inevitable, additionof procedural artifacts (e.g., scarring, etc.). In such examples, aphysician or physician's assistant may capture the set of pre-implantimages while placing a white balance card (e.g., representing purewhite) next to the body of patient 4. In an illustrative andnon-limiting example, a nurse or image acquisition representative may,prior to implantation of IMD 6, capture, via camera 32, one or more skincolor baseline images of an implantation site (e.g., an anticipatedimplantation site) of patient 4 while the image acquisitionsimultaneously or concurrently captures an image of a color reference,such as a white-balance reference card held within the frame.

Processing circuitry 20 may determine the set of pre-implant images witha color reference to allow processing circuitry 20 to identify a colortruth for the pigmentation and/or skin type of patient 4. Processingcircuitry 20 may store the set of pre-implant images to a storage device(e.g., a cloud storage device or storage device 24), where AI engine(s)28 and/or ML model(s) 30 may determine a color truth (e.g., via cloudsolutions) for patient 4. The color truth may be useful, as describedherein, in determining baseline characteristics of patient 4 and forprojecting healing characteristics of patient 4 over time based onimages captured post-implant. In this way, given the diversity in skintone and body type around the world, processing circuitry 20 may obtainimages of the implantation site prior to and immediately followingimplantation in order to accurately identify abnormalities (e.g.,excessive or abnormal bruising) at the implantation site across variouscohorts.

In some examples, the set of pre-implant images comprises a single imageor multiple images that represent the particular location of the body ofpatient 4 captured pursuant to particular lighting conditions or from aparticular vantage point relative to a set of potential vantage pointsrepresenting various different views of the implantation site. In anillustrative example, processing circuitry 20 may determine, from theset of pre-implant images, baseline characteristics of the body ofpatient 4 prior to the implantation. In some examples, the baselinecharacteristics may include pigmentation, skin type, etc.

In some examples, AI engine(s) 28 and/or ML model(s) 30 mayautomatically determine a micro cohort for patient 4 based on the set ofpre-implant images, such as a micro cohort based on skin pigmentation.In some examples, a user may, via UI 22, manually self-identify a cohortin which the user self-identifies, such as based on skin type orpigmentation. In another example, processing circuitry 20 may utilizemanually entered information and baseline characteristics data toautomatically identify a micro cohort for patient 4 that uses themanually-entered information as an assumption input to, for example, AIengine(s) 28 and/or ML model(s) 30 configured to automatically, or atleast semi-automatically, identify micro cohorts for further imageprocessing and/or for training purposes as described herein. In someexamples, AI engine(s) 28 and/or ML model(s) 30 may automatically infer,from a pre-process at the time of taking a first image of patient 4, acohort of patient 4 that will align the best results for analyzingimages of patient 4.

In an illustrative example, processing circuitry 20 may transmit, viacommunication circuitry 26, the pre-implant images to a device (e.g.,another one of computing device(s) 2) configured to operate a skin colorclassification algorithm for pre-processing. In another example,processing circuitry 20 may operate skin color classification algorithm,in which case the pre-implant images may only be stored to storagedevice 24. The algorithm may be trained on thousands of skin photos anduses a color scale, such as Von Luschan's chromatic scale, theFitzpatrick scale, or combinations thereof. Processing circuitry, e.g.,processing circuitry 20 of computing device(s) 2, processing circuitry64 of edge device(s) 12, processing circuitry 98 of server(s) 94, orprocessing circuitry 40 of medical device(s) 17, may utilize the colorscale in order classify a skin type of patient 4 into various skin typecategories so that ML model(s) 28 and/or AI engine(s) 30 may be able toaccurately identify abnormalities in the skin following implantation ofone of medical device(s) 17 (e.g., IMD 6). The skin type classificationmay include a classification or organization of the skin type of patient4 into skin-type subsets, such as various subsets corresponding toparticular color scales of the von Luschan scale and/or the Fitzpatrickscale, for example. In any case, processing circuitry 20 patient 4 mayautomatically segment patient 4 into a micro-cohort, for example, basedon the skin-type classification. That is, processing circuitry 20 mayautomatically determine the cohort for patient 4 based on one or morepre-implant images. in another example, processing circuitry 20 mayautomatically determine the cohort for patient 4 based on one or morepost-implant images (e.g., the first set of images and/or the second setof images).

In such examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may apply, in accordance with each cohort, aparticularly defined implantation-site analysis algorithm to yield bestresults for specific cohorts, such as automatically determined cohorts,by accounting for differences in skin-types. Additionally, during apponboarding, processing circuitry 20 may provide, via UI 22, patient 4with the option to self-identify their ethnicity, age, weight,biological sex and body-type. As described herein, if provided,processing circuitry 20 may use these inputs to fine-tune results of AIengine(s) 28 and/or ML model(s) 30.

Processing circuitry 20 may determine, based at least in part on thebaseline characteristics, the projection of alteration characteristics.In such examples, the alteration characteristics from the projection areconfigured to approximate, over time, the baseline characteristics ofthe body of the patient. That is, processing circuitry 20 may determinefrom a post-implant image a trajectory back toward a baseline of patient4, such as in terms of returning to a state that includes a sufficientlyhealed scar at an implantation site of IMD 6.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may identify a second set of images (1906). Inan example, processing circuitry 20 may identify the second set ofimages that represent the particular location of the body at aprogression interval that follows in time the first set of images.Similar to the first set of images, the second set of images may includea single image, while in other examples, the second set of images mayinclude multiple images. Likewise, processing circuitry 20 may perform asimilar validation for images of the second set of images, and maydiscard images that fail the validation test. In one example, processingcircuitry 20 may determine whether images are captured according to aparticular orientation (e.g., portrait, angled, etc.), and may promptthe user, via UI 22, when identifying an image captured at in improperorientation, to capture images according to the particular orientation.In another example, processing circuitry 20 may identify objects, viaobject recognition, and notify a user, via UI 22, when an imagerepresents an improper object, such as an object that does not appear torepresent a relevant implantation site.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may determine a second set of alterationcharacteristics (1908). In an example, processing circuitry 20 maydetermine, from the second set of images, the second set of alterationcharacteristics.

In an illustrative example, processing circuitry 20 may determine thefirst set of images so as to include at least one first post-implantimage and at least one second post-implant image, where those images arecaptured at successive time intervals. The successive time intervals maybe spaced apart in time by a first duration of time. In an example, theduration of time may include a relatively short period of time, such asan hour or a day. In such examples, the second set of images may includeat least one third post-implant image captured at a check-in time thatcomes after a time when the first set of images are captured.

In such examples, the second duration of time may be greater than thefirst duration of time, such as one week following the last capture ofthe first set of images. That is, the first set of images may becaptured at frequent intervals immediately following the implantation orat least following a time when bandages are removed from theimplantation site, such as every hour or every other hour for a firstnumber of days. The first set of images are captured at frequentintervals in order to determine how the characteristics of the imagesalter over time (e.g., healing indicators). In such examples, the secondset of images comprise a successive fourth image captured at a secondcheck-in time that follows a first check-in time by a third duration oftime, such as after two or three weeks following when capture of thefirst image of the baseline set. In such examples, the second set ofalteration characteristics comprises a healing trend of relativealteration characteristics between successive images of the second setof images. That is, the second set of alteration characteristics may bebased on a healing progression between at least two images of the secondset of images, where the healing progression may be compared to thefirst set of alteration characteristics determined from the first set ofimages and/or the pre-implant set of images. Processing circuitry 20 mayfurther determine the second set of alteration characteristics based onthe pre-implant set of images and/or the first set of images so as totrack a trajectory from the first set of images to the second set ofimages, from the second set of images to a baseline for patient 4 thatis based on the pre-implant images, and/or a combination of suchtrajectories.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may compare the second set of alterationcharacteristics to the projection of alteration characteristics (1910).In an example, processing circuitry 20 may compare the second set ofalteration characteristics to the projection. In an illustrativeexample, processing circuitry 20 may, to compare the second set ofalteration characteristics to the projection, determine a differenceamount from the second set of alteration characteristics and theprojection. In some examples, processing circuitry 20 may determine adifference amount from the second set of alteration characteristics andthe projection by comparing the second set of alteration characteristicsto a particular portion of the projection that corresponds to aprojected time corresponding to the second set of images (e.g., a timewhen the second set of images was identified, such as when the secondset of images was acquired, captured, received, timestamped,communicated from another device, and/or obtained), where the projectedtime is projected based at least in part on intervals or points in timethat correspond to a historical set of images (e.g., the first set ofimages, pre-implant images, the first set of images and pre-implantimages, etc.).

In a non-limiting example, processing circuitry 20 may determine aprojection that indicates that, based on a historical set of images, aparticular alteration should occur relative to an implantation site ofpatient 4 at a particular time in the future (e.g., a projected time).Processing circuitry 20 may, in some instances, prompt patient 4 tocapture a second set of images (e.g., one image or a plurality ofimages) at the projected time and may compare characteristics of thesecond set of images against the projection to determine to what degreea healing of patient 4 is tracking onto the projection. Where thedifference between the projection and the second set of characteristicsexceeds a particular threshold, processing circuitry 20 may determinethat a closer analysis is warranted or that additional images should becaptured of the implantation site for a supplemental or enhancedanalysis. In another example, processing circuitry 20 may, rather thanprompt patient 4 for images, may identify the second set of images anddetermine from the projection, a particular portion from the projectionthat coincides with the second set of images (e.g., coinciding in termsof time intervals). In any case, processing circuitry may compare thesecond set of images to the projection to determine the presence of anabnormality, such as where the comparison indicates a particulardeviation amount that exceeds a predetermined threshold.

In some instances, processing circuitry 20 may determine, from ahistorical set of images, a projection as to how much the implantationsite of patient 4 should be altering over time at a particular time inthe future (e.g., a prospective time). In such instances, processingcircuitry 20 may compare the second set of alteration characteristics tothe projection to identify a potential abnormality, the likelihood of apotential abnormality, or whether further analysis is warranted, such asan analysis by an HCP, technician, etc. In another example, processingcircuitry 20 may determine, from the second set of images and in someinstances, from the first set of images, how much the implantation siteof patient 4 is or appears to be altering over time at a timecorresponding to the second set of images. Processing circuitry 20 maydetermine the second set of alteration characteristics from thedetermination as to how much the implantation site of patient 4 is orappears to be altering over time, and processing circuitry 20 maycompare the second set of alteration characteristics to the prospectiveprojection, where the prospective projection is indicative as to howmuch processing circuitry 20 expects the implantation site of patient 4to be altering over time at a particular time from the projection thataligns with, or at least approximates, the time corresponding to one ormore images from the second set of images (e.g., the time at which thesecond set of images were obtained).

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may output, via UI 22, an estimate as to whenone or more milestone alterations of the particular location of the bodyare expected according to the projection. In an example, the milestoneestimates may include an indication as to when redness should subside,when soreness should subside below a predefined threshold relative topain thresholds of patient 4, when likelihood of infection has droppedbelow a predefined threshold, when implantation site is healed beyond apredefined healing threshold, etc. In addition, processing circuitry 20may determine the milestone estimates based on a comparison of thesecond set of alteration characteristics to the projection. Thecomparison, in such examples, is indicative of a deviation amount thatprocessing circuitry 20 may utilize in order to determine an estimatethat is informed by current images of patient 4 and past images ofpatient 4. In addition, processing circuitry 20 may determine theestimate based on (e.g., as informed by) images and/or healing trends ofother IMD patients (e.g., patient of a same micro cohort as patient 4).In any case, the deviation amount may indicate a degree to which ahealing of patient 4 has deviated from the projection (e.g., aprojection based on historical images of patient 4 and/or other IMDpatients) as informed by a determination of the second set of alterationcharacteristics. As such, processing circuitry 20 may provide aninformed estimate as to when patient 4 can expect to reach variousalteration milestones of a healing process of patient 4.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may identify the potential abnormality from thecomparison (1912). In an example, processing circuitry 20 may identify,based at least in part on the comparison, a potential abnormality at theparticular location of the body. In some examples, processing circuitry,e.g., processing circuitry 20 of computing device(s) 2, processingcircuitry 64 of edge device(s) 12, processing circuitry 98 of dataserver(s) 94, or processing circuitry 40 of medical device(s) 17 mayidentify the potential abnormality by determining an abnormality trendindicating an anticipated progression toward the potential abnormalityor toward a worsening abnormality relative to the potential abnormality.In any case, in response to determining the potential abnormality,processing circuitry 20 may transmit (e.g., via communication circuitry26) the first set of images and the second set of images to a device ofone or more HCPs over network 10. In another example, processingcircuitry 20 may utilize the abnormality determination to determine apost-implant report that, in some examples, includes a basis inphysiological parameter data, device interrogation data, etc.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may identify (e.g., via an inference engine) oneor more abnormality control procedures corresponding to the at least onecomponent of the IMD (e.g., gauze, TYRX™, etc.) or corresponding topatient 4 (e.g., medications, etc.). In an example, processing circuitry20 may determine such information via patient-input during apatient-input subsession. That is, a user (e.g., patient 4) may entermedication information, gauze information, etc., that processingcircuitry 20 may reference, via AI inference engine, in order todetermine, based at least in part on the one or more abnormality controlprocedures, the projection data. In such examples, the projection for ahealing timeline and the trend towards healing may differ, such as wherea TYRX™ control procedure is used or where particular medications areused.

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of data server(s) 94, or processing circuitry 40of medical device(s) 17 may output a result of the comparison to astorage device (e.g., storage device 24, storage device 65, storagedevice 96, and/or storage device 50). In an example, processingcircuitry 20 may transmit, as part of outputting the second set ofimages to one or more HCPs, abnormality data items configured tovisually represent the potential abnormality identified in the secondset of images. In an illustrative example, where processing circuitry 20labels a result for an image as comprising a ‘potential infection,’processing circuitry 20 may generate an alert and provide an alertindication to one or more HCPs (e.g., via UI 22). The alert indicationmay include a summary of the result, a post-implant report, one or moreimages, and in some instances, highlighting of the images to indicatecharacteristics of the potential abnormality. In addition, the alertindication may include a time-lapse of images created in accordance withone or more of the various techniques of this disclosure. In any case,processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of data server(s) 94, or processing circuitry 40 of medicaldevice(s) 17 may maintain (e.g., via a respective storage device) achronology of the first set of images.

In some examples, the projection techniques of this disclosure allowprocessing circuitry 20 to personalize the algorithm and make feedbackmost useful to patient 4 and clinician. This is because images of theimplantation site may be captured at high frequency immediatelyfollowing implant, thus, allowing processing circuitry 20 to produce atime-series (e.g., a time lapse) set of data. Processing circuitry 20may then utilize the time-series to create a personalized baseline forthe patient and allow a model to produce trend analysis, as describedherein. In another example, processing circuitry 20 may utilize apreprocessing algorithm to auto-adjust all photos to scale andorientation so that physicians reviewing time-series get a consistentview as the HCP reviews historical images, seemingly, through time. Insuch examples, processing circuitry 20 may provide scheduled remindersprompting the user to capture particular images (e.g., with particularzoom levels, lighting, angles, etc.) at a first frequency in order todetermine a first set of reference images (e.g., the first set ofpost-implant images), and may provide scheduled reminders prompting theuser to capture particular images at a second frequency, where thesecond frequency may include a variable frequency, but in general, mayinclude a frequency that is less than the first frequency, in order todetermine the second set of ongoing check-in images (e.g., the secondset of post-implant images). Based on at least the two sets of images,processing circuitry 20 may accurately determine the presence ofabnormalities and/or determine post-implant reports that indicateabnormalities as informed by other sets of data items (e.g.,physiological parameters, etc.) It should be noted that, as describedherein, the various techniques of this disclosure (e.g., the projectiontechniques, the subsession techniques, overlay techniques, etc.) areapplicable to both a “cloud” implementation (e.g., Medtronic CareLink®Network) and “edge” implementation (e.g., on the mobile app, tablet app,IoT app, etc.).

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may later synchronize, for ease of access and/oralgorithm tuning, the data and results of the image analysis. In oneexample, processing circuitry 20 may coordinate with other processors ofthe processing system to synchronize data and results of the imageanalysis between an edge network (e.g., computing device(s) 2, edgedevice(s) 12, medical device(s) 17, etc.) and a cloud network (e.g.,server(s) 94).

FIG. 20 is a flowchart illustrating an example method of navigating aset of UI interfaces of a virtual check-in process (e.g., an interactivesession), in accordance with one or more techniques of this disclosure.Processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may determine, in accordance with the site-checksubsession, data item(s) comprising image data representing the body ofpatient 4 (2002). In another examples, processing circuitry, e.g.,processing circuitry 20 of computing device(s) 2, processing circuitry64 of edge device(s) 12, processing circuitry 98 of server(s) 94, orprocessing circuitry 40 of medical device(s) 17, may determine, inaccordance with a physiological-parameter subsession, data item(s)comprising physiological parameters (2004). In an optional example,processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may determine, in accordance with a patient-statussubsession, data item(s) comprising patient input (2006). As describedherein, processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may determine, in accordance with a device-checksubsession, data item(s) comprising device interrogation data (2008). Itshould be understood that the subsessions may be performed in variousdifferent sequences and with additional subsession or without one ormore of the subsession described herein. In any case, processingcircuitry, e.g., processing circuitry 20 of computing device(s) 2,processing circuitry 64 of edge device(s) 12, processing circuitry 98 ofserver(s) 94, or processing circuitry 40 of medical device(s) 17, maydetermine an abnormality from the combination and/or synthesis of imagedata and one or more of the other data item(s) described herein (2010).

FIG. 21 is a UI visualization of an example complete check-in interface2102, in accordance with one or more techniques of this disclosure.Complete check-in interface 2102 illustrates a UI that processingcircuitry 20 may cause to be displayed upon completion of one or more ofthe virtual check-in subsessions (e.g., device-check subsession,site-check process, etc.). In some instances, processing circuitry 20may output the complete check-in interface 2102 after the information issubmitted that is to be forwarded to the HCP/clinician, etc. FIG. 21illustrates a UI that computing device(s) 2 may generate and present toindicate completion of the interactive session corresponding to all ofthe four tiles shown in FIG. 7. As described herein, the completecheck-in interface 2102 may relate to lesser or greater number of tilesin some examples, where computing device(s) 2 utilize additional orfewer interfaces to obtain data regarding patient 4 and/or medicaldevice(s) 17. Complete check-in interface 2102 may further include agraphical icon 2104 indicating completion of, for example, thesite-check subsession.

In some examples, the image(s) may be adjudicated by a teledermatologyservice. In such instances, the teledermatology service may beconfigured to generate a report (e.g., a summary report, post-implantreport, etc.) based on the image(s). In addition, computing device(s) 2may receive the report from the teledermatology service and provide, viaUI 22, the report for review. In such examples, computing device(s) 2may interface with the teledermatology service via network 10 and/or viaan intermediary, such as via one of edge device(s) 12. As describedherein, edge device(s) 2 may include one of computing device(s) 2, wherethe computing device 2 is configured to interface with and/or operatethe teledermatology service in order to generate, based on the variousimages, a summary report for output.

In some instances, processing circuitry, e.g., processing circuitry 20of computing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may generate a summary report that matches aproficiency level of a user. In one example, processing circuitry 20 mayidentify a particular proficiency level of a user and generate a summaryreport for the user in accordance with the proficiency level. Theproficiency level, in this instance, may represent the proficiency of auser in reviewing reports, such as teledermatology reports. Processingcircuitry 20 may determine this proficiency level automatically fromuser data and/or may receive user input indicating this proficiencylevel, such as by requesting such information from the user via aquestionnaire or other input mechanism.

In an illustrative example, processing circuitry 20 may receive a singlereport data file. Processing circuitry 20 may then transform the data ofthe data file in order to generate a user-facing summary report (e.g., apost-implant report). Processing circuitry 20 may transform the datadifferently, for example, when processing circuitry 20 determines thereport is being presented to a “novice” user (e.g., an elderly patienttaking photos themselves) as compared to being presented to another use,such as an “expert” user (e.g., particular HCPs, nursing home workers,etc.). The summary report may include results of a HCP adjudication ofimages, physiological parameters, patient-status updates, deviceinterrogation data (e.g., device diagnostics) and/or the results of anautomated image analysis of the interactive reporting session.

In some examples, complete check-in interface 2102 may provide an optionto schedule an in-person clinic follow-up. Processing circuitry 20 maycause this option to be presented, via UI 22, when a potentialabnormality is detected or where processing circuitry 20 was unable torule out an abnormality from an assessment of the camera images.

In addition, complete check-in interface 2102 may include a “generatereport” icon 2106 and/or a “send report to clinic” icon 2108. The reportmay include, among other information, the image(s) captured byprocessing circuitry 20. In addition, the report may include a synthesisof data including criteria for failure based on severity of failure(e.g., criteria for finding an abnormality), number of failed tests,number of failed attempts, number of tiles failed, etc. In addition,processing circuitry 20 may generate the report based on a physicianpreference (e.g., type of report, specifics for data weighting, etc.).Processing circuitry 20 may, in addition, transmit the report based on aphysician preference regarding notifications and/or notificationfrequency. Processing circuitry 20 may further synthesize the data basedon characteristics of patient 4 and/or type of medical device 17 (e.g.,IMD 6).

In an illustrative example, processing circuitry 20 may employ variousabnormality scoring algorithms that are based on a severity of apotential abnormality detected in one or more images. The abnormalityscoring algorithm may determine an abnormality score that maps to anactionable response (e.g., transmit report to HCP, automaticallyschedule clinician visit, etc.), based on IMD type, etc. The sensitivitymay define a threshold that can range from a conservative threshold thatcauses a particular output (e.g., failure determination, transmit reportto HCP, etc.) in response to a determination of any subsession failure.In another example, the sensitivity may define a complex threshold thatutilizes a weighted composite score that, in a non-limiting example,factors the severity of the failure across the subsessions, along withthe underlying algorithms that define each subsession (whether failed ornot), relative to any underlying medical conditions of patient 4, todetermine if a failure is determined that warrants a particularresponse, e.g., based on a mapping of an abnormality score to particularresponse outputs. In addition, the sensitivity may further define athreshold that factors in physician programmable limits for medicaldevice(s) 17.

In some examples, processing circuitry 20 may provide feedback topatient 4 through graphical simplified icons. In another example,processing circuitry 20 may obtain a preference from a HCP thatindicates, rather than simplified icons, to provide complex results. Insuch instances, processing circuitry 20 may provide feedback to patient4 as complex results.

FIG. 22 is a UI visualization of an example complete check-in interface2202, in accordance with one or more techniques of this disclosure. Inan example, complete check-in interface 2202 indicates a confirmation ofreceipt 2204, either by the HCP's office or by an intermediate systemthat is responsible for submission to the HCP's office. That is,processing circuitry 20 may receive confirmation from an intermediatesystem (e.g., edge device(s) 12) and in turn, may provide receiptconfirmation 2204. Receipt confirmation 2204 may represent receipt ofthe post-implant report at the office of a HCP of patient 4. In anotherexample, receipt confirmation 2204 may represent confirmation that thepost-implant report has been saved successfully to a database (e.g., oneof server(s) 94, a plurality of server(s) 94 comprising a cloud storagedatabase, server(s) 94 and edge device(s) 12 comprising a cloud storagedatabase, etc.). In such examples, authorized HCP may, via computingdevice 2 of the HCP, access the report from the database. In anillustrative example, receipt confirmation may further include uploadingthe summary report to an EMR database. As described herein, thetechniques of this disclosure are applicable to both a “cloud”implementation (e.g., Medtronic CareLink® Network) and/or “edge”implementation (e.g., on a mobile app). As such, the post-implant reportmay be uploaded and stored in any number of different locations andaccessed from those locations in any number of different ways.

FIG. 23 is a flowchart illustrating an example method of determininginstructions for medical intervention concerning an IMD patient, inaccordance with one or more techniques of this disclosure. In someexamples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may determine, from a post-implant report, ahealth condition status of at least one of medical device(s) 17 (e.g.,IMD(s) 6) and/or patient 4 (2302). The health condition status mayinclude an indication that processing circuitry 20 has determined anabnormality at the implantation site of IMD 6 and/or an abnormality withthe performance of one or more medical device(s) 17 (e.g., IMD 6), anabnormality in a physiological parameter, and/or an abnormality with apatient-status input. In some instances, processing circuitry 20 maydetermine the health condition status based on a synthesis (e.g.,combination) of data items obtained via a plurality of subsessions(e.g., a site-check subsession and a physiological parameter subsession)of an interactive check-in session. In some examples, processingcircuitry 20 determines the health condition status based on imagesreceived via UI 22. Although described as being generally performed bycomputing device(s) 2, the example method of FIG. 23 may be performedby, e.g., any one or more of edge device(s) 12, medical device(s) 17, orserver(s) 94, e.g., by the processing circuitry of any one or more ofthese devices.

Processing circuitry, e.g., processing circuitry 20 of computingdevice(s) 2, processing circuitry 64 of edge device(s) 12, processingcircuitry 98 of server(s) 94, or processing circuitry 40 of medicaldevice(s) 17, may determine instructions for medical intervention basedon the health condition status of patient 4 (2304). For example, whereprocessing circuitry 20 determines the presence of an abnormality at theimplantation site of IMD(s) 6, processing circuitry 20 may determineinstructions for medical intervention based on the abnormality. Inanother example, where processing circuitry 20 determines the presenceof an abnormality at the implantation site of IMD(s) 6 and anabnormality in a physiological parameter of patient 4, processingcircuitry 20 may determine instructions for medical intervention basedon a post-implant report that processing circuitry 20 may generate basedat least in part on the multiple abnormalities. In some examples,processing circuitry 20 may determine different instructions fordifferent severity levels or abnormality categories. For example,processing circuitry 20 may determine a first set of instructions forone abnormality that processing circuitry 20 determines is likely lesssevere than another abnormality. In some examples, processing circuitry20 may not determine intervention instructions where processingcircuitry 20 determines that the abnormality level does not satisfy apredefined threshold. In some examples, processing circuitry 20 mayprovide an alert, such as a text- or graphics-based notification, avisual notification, etc. In some examples, processing circuitry 20 maycause an audible alarm to sound or cause a tactile alarm, alertingpatient 4 of a determined abnormality. In other examples, computingdevice(s) 2 may provide a visual light indication, such as emitting ared light for high severity or a yellow light for medium severity. Thealert may indicate a potential, possible or predicted abnormality event(e.g., a potential infection).

In some examples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may transmit the instructions for medicalintervention to be display via a user interface, such as UI 22 (2306).In some examples, processing circuitry 20 may transmit the instructionsto a device of a HCP (e.g., a caretaker), such as a pager of the HCP. Inexamples where processing circuitry 64 generates the instructions,processing circuitry 20 may transmit the instructions for medicalintervention to a user interface, such as UI 22. The instructions mayinclude the post-implant report and/or individual abnormalityindications (e.g., ECG abnormalities, etc.). In some examples, edgedevice(s) 12, medical device(s) 17 (e.g., IMD 6), server(s) 94, and/orcomputing device(s) 2 may use the gathered data to predict adversehealth events (e.g., worsening infections) using integrated diagnosticmethods. That is, computing device(s) 2 may use the abnormalitydetermination, including a likelihood or severity determination, as anevidence node of a probability model deployed, for example, by AIengine(s) 28 and/or ML model(s) 30, in order to determine a probabilityscore indicating the likelihood that an implantation site of patient 4is infected, is likely to become infected within a predetermined amountof time, that one of medical device(s) 17 (e.g., IMD 6) is likely toexperience a functional abnormality (e.g., a malfunction), etc. In someexamples, processing circuitry, e.g., processing circuitry 20 ofcomputing device(s) 2, processing circuitry 64 of edge device(s) 12,processing circuitry 98 of server(s) 94, or processing circuitry 40 ofmedical device(s) 17, may further include, as evidence nodes for aprobability determination (e.g., abnormality prediction), criteria forfailure based on severity of failure (e.g., criteria for finding anabnormality), number of failed tests, number of failed attempts, numberof tiles or subsessions failed, patient characteristics, type of medicaldevice(s) 17 (e.g., IMD 6), physician preferences, etc.

While described from the perspective of a user computing device 2performing the techniques of this disclosure, it should be noted thatthe system of this disclosure supports bidirectional communicationbetween a user (e.g., patient 4) and an HCP. The bidirectionalcommunication may function using similar UIs on both ends of thecommunication line. In such examples, an HCP may, via computing device 2of the HCP, access images uploaded by the user via computing device 2 ofthe user, physiological parameter data items, medical deviceinformation, patient information, etc. In addition, the HCP may, viacomputing device 2 of the HCP, determine the presence or absence of anabnormality from the data upload, and transmit a summary report back tocomputing device 2 of the user (e.g., patient 4), for example, includingone or more indications of an abnormality.

Illustrative examples of the disclosure include:

Example 1

A method of monitoring a patient of an IMD, the method comprising:providing, via a computing device, an interactive session configured toallow a user to navigate a plurality of subsessions comprising at leasta first subsession and a second subsession distinct from the firstsubsession, wherein the first subsession comprises capturing image datavia one or more cameras; determining, via the computing device, a firstset of data items in accordance with the first subsession of theinteractive session, the first set of data items including the imagedata; determining, via the computing device, a second set of data itemsin accordance with the second subsession of the interactive session, thesecond set of data items distinct from the first set of data items andcomprising one or more of: data obtained from the IMD, at least onephysiological parameter of the patient, or user-input data; determining,based at least in part on the first set of data items and the second setof data items, an abnormality corresponding to at least one of thepatient or the IMD; and outputting, via the computing device, apost-implant report of the interactive session, wherein the post-implantreport includes indication of the abnormality and indication of anamount of time that has transpired since the date of implantation of theIMD.

Example 2

A method according to Example 1, wherein determining the post-implantreport comprises: determining, from the second set of data items, theabnormality; and determining, based at least in part on the abnormalityand the first set of data items, the post-implant report.

Example 3

A method according to any of Examples 1 or 2, wherein providing theinteractive session comprises: training, via the computing device, asession generator on one or more of: cohort parameters or IMDinformation; deploying, via the computing device, the session generatorto generate the interactive session; and providing, via the sessiongenerator, the interactive session that, as a result of the training, isat least in part personalized for the user.

Example 4

A method according to any of Examples 1 through 3, wherein the IMDinformation includes interrogation data, and wherein the method furthercomprises: performing, via the computing device, a pairing process, thepairing process configured to pair the computing device with the IMD;and receiving, via the pairing process, the interrogation data regardingthe IMD; and storing, via the computing device, the interrogation dataas historical interrogation data for reference.

Example 5

A method according to any of Examples 1 through 4, wherein providing theinteractive session comprises: identifying a follow-up schedule for thepatient, the follow-up schedule defined by one or more time periods inwhich the computing device is configured to prompt the user to conductthe interactive session; and providing the interactive session inaccordance with the follow-up schedule.

Example 6

A method according to Example 5, wherein identifying the follow-upschedule comprises: receiving, via communication circuitry of thecomputing device, a push notification; and determining, based on thepush notification, a first time period for the follow-up schedule.

Example 7

A method according to any of Examples 5 or 6, the one or more timeperiods include at least one time period that corresponds to apredetermined amount of time from a date of implantation of the IMD.

Example 8

A method according to any of Examples 1 through 7, wherein the imagedata comprises one or more frames that represent images of a body of thepatient.

Example 9

A method according to Example 8, wherein a portion of the body of thepatient comprises an implantation site of the IMD, and wherein the oneor more frames represent images of the implantation site.

Example 10

A method according to any of Examples 1 through 9, wherein providing theinteractive session comprises: providing a top-level interface thatincludes a first interface tile, wherein the first interface tilecorresponds to the first subsession, and wherein the first subsessioncomprises a first subinterface level that is at a lower level relativeto a level of the top-level interface.

Example 11

A method according to Example 10, wherein the first set of data itemscomprises an image overlay, and wherein the method further comprises:outputting, via the computing device, the image overlay at the firstsubinterface level.

Example 12

A method according to any of Examples 10 or 11, wherein providing thefirst subsession comprises: detecting, via the computing device,selection of the first interface tile; and providing, via the computingdevice, the first subsession of the interactive session.

Example 13

A method according to Example 12, wherein determining the first set ofdata items comprises: providing, via the computing device, a prompt forthe user to utilize the one or more cameras to capture the image data;and determining, subsequent to the prompt, at least a portion of thefirst set of data items.

Example 14

A method according to any of Examples 10 through 13, wherein providingthe second subsession comprises: detecting, via the computing device,selection of a second interface tile, wherein the first interface tileand the second interface tile are distinct from one another; andproviding, in response to selection of the second interface tile, thesecond subsession of the interactive session.

Example 15

A method according to Example 14, wherein the top-level interfaceincludes the second interface tile.

Example 16

A method according to any of Examples 14 or 15, further comprising:providing, via a second subsession interface, the second subsession ofthe interactive session; modifying the second interface tile to indicatecompletion of the second subsession; providing, subsequent to the secondsubsession, the first subsession of the interactive session; modifyingthe first interface tile to indicate completion of the first subsession;providing, subsequent to the first subsession or the second subsession,a third subsession of the interactive session; and determining, via thecomputing device, a third set of data items in accordance with the thirdsubsession of the interactive session, the third set of data itemsdistinct from the first set of data items.

Example 17

A method according to Example 16, wherein determining the third set ofdata items comprises: receiving physiological parameter signals; anddetermining, from the physiological parameter signals, the third set ofdata items.

Example 18

A method according to any of Examples 1 through 17, wherein theabnormality comprises a first abnormality, and wherein determining thepost-implant report comprises: outputting, via communication circuitryof the computing device, the second set of data items to another device;receiving, via communication circuitry of the computing device, a resultof an analysis of the third set of data items, the result indicatingthat the second set of data items does not indicate the presence of asecond abnormality; and determining the post-implant report based atleast in part on the first abnormality and the second set of data items.

Example 19

A method according to any of Examples 1 through 15, wherein the secondsubsession comprises a physiological-parameter subsession, whereindetermining the second set of data items comprises: receiving, inaccordance with the second subsession, at least one physiologicalparameter corresponding to the patient; and determining, from the atleast one physiological parameter, the second set of data items.

Example 20

A method according to Example 19, wherein receiving the at least onephysiological parameter comprises: receiving, via communicationcircuitry of the computing device, the at least one physiologicalparameter from a plurality of medical devices.

Example 21

A method according to any of Examples 19 or 20, wherein the at least onephysiological parameter comprises at least one of: an electrocardiogram(ECG) parameter, a respiration parameter, an impedance parameter, anactivity parameter, or a pressure parameter.

Example 22

A method according to Example 21, wherein the ECG parameter representsan abnormal ECG, and wherein determining an abnormality comprises:

-   -   determining, based at least in part on the abnormal ECG, the        abnormality.

Example 23

A method according to any of Examples 1 through 15, wherein the secondsubsession comprises a device-check subsession, wherein determining thesecond set of data items comprises: performing, via communicationcircuitry of the computing device, an interrogation of one or moremedical devices corresponding to the patient, wherein the one or moremedical devices include the IMD; and determining, from theinterrogation, the second set of data items.

Example 24

A method according to any of Example 23, wherein performing theinterrogation comprises: receiving, via a computing network, the secondset of data items.

Example 25

A method according to any of Examples 23 or 24, wherein determining thepost-implant report comprises: determining, from the second set of dataitems, that the one or more medical devices satisfy one or moreperformance thresholds; and determining, based at least in part on thesecond set of data items, the post-implant report.

Example 26

A method according to any of Examples 1 through 3, wherein the secondsubsession comprises a patient-status subsession, wherein determiningthe second set of data items comprises: receiving user-input data inaccordance with the second subsession of the interactive session; anddetermining, from the user-input data, the second set of data items.

Example 27

A method according to Example 26, wherein the user-input data comprisesone or more of: patient-entered data, medication information, symptominformation, physiological metrics, or anatomical metrics.

Example 28

A method according to any of Examples 26 or 27, wherein the interactivesession comprises a third subsession and a fourth subsession, whereinthe third subsession comprises a physiological-parameter subsession andthe fourth subsession comprises a device-check subsession, whereindetermining the abnormality comprises: determining, in accordance withthe third subsession, a third set of data items, the third set of dataitems comprising the at least one physiological parameter of thepatient; determining, in accordance with the fourth subsession, a fourthset of data items, the fourth set of data items comprising interrogationdata; and determining the abnormality based at least in part on thethird set of data items or the fourth set of data items.

Example 29

A method according to any of Examples 1 through 28, wherein providingthe second subsession comprises: initiating, by the computing device,the second subsession subsequent to the first subsession.

Example 30

A method according to any of Examples 1 through 29, wherein determiningthe first set of data items comprises: receiving, via communicationcircuitry of the computing device, the first set of data items, andwherein determining the second set of data items comprises: receiving,via the computing device, the second set of data items.

Example 31

A method according to any of Examples 1 through 15, wherein the secondset of data items comprises information indicative of: an abnormality ofthe at least one physiological parameter of the patient or anabnormality of a device parameter corresponding to one or more medicaldevices.

Example 32

A method according to any of Examples 1 through 31, wherein determiningindication of the abnormality comprises: outputting, to an abnormalitydeterminer for an abnormality analysis, at least one of the first set ofdata items or the second set of data items; and determining, by thecomputing device, a result of the abnormality analysis, wherein theresult indicates the abnormality.

Example 33

A method according to Example 32, wherein the abnormality determinerincludes at least one of an AI engine or a ML model.

Example 34

A method according to any of Examples 32 or 33, wherein the computingdevice includes the abnormality determiner, and wherein determining theabnormality comprises: deploying, by the computing device, theabnormality determiner to determine the abnormality, wherein theabnormality determiner is trained to identify, based at least in part onthe image data, the abnormality.

Example 35

A method according to any of Examples 1 through 23, wherein outputtingthe post-implant report comprises: outputting, by the computing device,the post-implant report to a device of a HCP via a computing network.

Example 36

A method according to any of Examples 1 through 35, wherein the user isthe patient.

Example 37

A method according to any of Examples 1 through 36, determining, by thecomputing device, that the interactive session is complete; andproviding a notification, via the computing device, that the interactivesession is complete.

Example 38

A method according to any of Examples 1 through 37, wherein outputtingthe post-implant report comprises: outputting, by the computing device,a result of the post-implant report for display.

Example 39

A system for monitoring a patient of an IMD, the system comprising oneor more means for performing the methods of any of Examples 1 through38. For example, the system of Example 39 may include a memoryconfigured to store image data; and one or more processors implementedin circuitry and configured to: provide an interactive sessionconfigured to allow a user to navigate a plurality of subsessionscomprising at least a first subsession and a second subsession distinctfrom the first subsession, wherein the first subsession comprisescapturing the image data via one or more cameras; determine a first setof data items in accordance with the first subsession of the interactivesession, the first set of data items including the image data; determinea second set of data items in accordance with the second subsession ofthe interactive session, the second set of data items distinct from thefirst set of data items and comprising one or more of: data obtainedfrom the IMD, at least one physiological parameter of the patient, oruser-input data; determine, based at least in part on the first set ofdata items and the second set of data items, an abnormalitycorresponding to at least one of the patient or the IMD; and output apost-implant report of the interactive session, wherein the post-implantreport includes indication of the abnormality and indication of anamount of time that has transpired since the date of implantation of theIMD.

Example 40

A system according to Example 39, wherein to determine the post-implantreport, the one or more processors are configured to: determine, fromthe second set of data items, the abnormality; and determine, based atleast in part on the abnormality and the first set of data items, thepost-implant report.

Example 41

A system according to any of Examples 39 or 40, wherein to provide theinteractive session, the one or more processors are configured to: traina session generator on one or more of: cohort parameters or IMDinformation; deploy the session generator to generate the interactivesession; and provide the interactive session that, as a result of thetraining, is at least in part personalized for the user.

Example 42

A system according to any of Example 41, wherein the IMD informationincludes interrogation data, and wherein the one or more processors arefurther configured to: perform a pairing process, the pairing processconfigured to pair a mobile device with the IMD; and receive, via thepairing process, the interrogation data regarding the IMD; and store theinterrogation data as historical interrogation data for reference.

Example 43

A system according to any of Examples 39 through 42, wherein to providethe interactive session, the one or more processors are configured to:identify a follow-up schedule for the patient, the follow-up scheduledefined by one or more time periods in which the computing device isconfigured to prompt the user to conduct the interactive session; andprovide the interactive session in accordance with the follow-upschedule.

Example 44

A system according to Example 43, wherein to identify the follow-upschedule, the one or more processors are configured to: receive, viacommunication circuitry, a push notification; and determine, based onthe push notification, a first time period for the follow-up schedule.

Example 45

A system according to any of Examples 43 or 44, wherein the one or moretime periods include at least one time period that corresponds to apredetermined amount of time from the date of implantation of the IMD.

Example 46

A system according to any of Examples 39 through 45, wherein the imagedata comprises one or more frames that represent images of a body of thepatient.

Example 47

A system according to Example 46, wherein a portion of the body of thepatient comprises an implantation site of the IMD, and wherein the oneor more frames represent images of the implantation site.

Example 48

A system according to any of Examples 39 through 47, wherein to providethe interactive session, the one or more processors are configured to:provide a top-level interface that includes a first interface tile,wherein the first interface tile corresponds to the first subsession,and wherein the first subsession comprises a first subinterface levelthat is at a lower level relative to a level of the top-level interface.

Example 49

A system according to Example 48, wherein the first set of data itemscomprises an image overlay, and wherein the one or more processors arefurther configured to: output the image overlay at the firstsubinterface level.

Example 50

A system according to Example 49, wherein the image overlay comprises astatic image overlay retrieved from an overlay library comprising the atleast one image overlay.

Example 51

A system according to any of Examples 49 or 50, wherein the one or moreprocessors are further configured to: determine, based at least in parton patient data, IMD information, and/or a template overlay, a customimage overlay as the image overlay.

Example 52

A system according to any of Examples 48 through 51, wherein to providethe first subsession, the one or more processors are configured to:detect selection of the first interface tile; and provide the firstsubsession of the interactive session.

Example 53

A system according to any of Examples 39 through 52, wherein todetermine the first set of data items, the one or more processors areconfigured to: provide, via a user interface (UI), a prompt for the userto utilize the one or more cameras to capture the image data; anddetermining, subsequent to the prompt, at least a portion of the firstset of data items.

Example 54

A system according to any of Examples 48 through 53, wherein to providethe second subsession, the one or more processors are configured to:detect selection of a second interface tile, wherein the first interfacetile and the second interface tile are distinct from one another; andprovide, in response to selection of the second interface tile, thesecond subsession of the interactive session.

Example 55

A system according to Example 54, wherein the top-level interfaceincludes the second interface tile.

Example 56

A system according to any of Examples 54 or 55, wherein the one or moreprocessors are further configured to: provide, via a second subsessioninterface, the second subsession of the interactive session; modify thesecond interface tile to indicate completion of the second subsession;provide, subsequent to the second subsession, the first subsession ofthe interactive session; modify the first interface tile to indicatecompletion of the first subsession; provide, subsequent to the firstsubsession or the second subsession, a third subsession of theinteractive session; and determine a third set of data items inaccordance with the third subsession of the interactive session, thethird set of data items distinct from the first set of data items.

Example 57

A system according to Example 56, wherein to determine the third set ofdata items, the one or more processors are configured to: receivephysiological parameter signals; and determine, from the physiologicalparameter signals, the third set of data items.

Example 58

A system according to any of Examples 39 through 57, wherein theabnormality comprises a first abnormality, and wherein to determiningthe post-implant report, the one or more processors are configured to:output the second set of data items to another device; receive a resultof an analysis of the third set of data items, the result indicatingthat the second set of data items does not indicate the presence of asecond abnormality; and determine the post-implant report based at leastin part on the first abnormality and the second set of data items.

Example 59

A system according to any of Examples 39 through 55, wherein the secondsubsession comprises a physiological-parameter subsession, wherein todetermine the second set of data items, the one or more processors areconfigured to: receive, in accordance with the second subsession, atleast one physiological parameter corresponding to the patient; anddetermine, from the at least one physiological parameter, the second setof data items.

Example 60

A system according to Example 59, wherein to receive the at least onephysiological parameter, the one or more processors are configured to:receive the at least one physiological parameter from a plurality ofmedical devices.

Example 61

A system according to any of Examples 59 or 60, wherein the at least onephysiological parameter comprises at least one of: an ECG parameter, arespiration parameter, an impedance parameter, an activity parameter, ora pressure parameter.

Example 62

A system according to Example 61, wherein the ECG parameter representsan abnormal ECG, and wherein to determine an abnormality, the one ormore processors are configured to: determine, based at least in part onthe abnormal ECG, the abnormality.

Example 63

A system according to any of Examples 39 through 55, wherein the secondsubsession comprises a device-check subsession, wherein to determine thesecond set of data items, the one or more processors are configured to:perform an interrogation of one or more medical devices corresponding tothe patient, wherein the one or more medical devices include the IMD;and determine the second set of data items.

Example 64

A system according to Example 63, wherein to perform the interrogation,the one or more processors are configured to: receive, via a computingnetwork, the second set of data items.

Example 65

A system according to any of Examples 63 or 64, wherein to determine thepost-implant report, the one or more processors are configured to:determine, from the second set of data items, that the one or moremedical devices satisfy one or more performance thresholds; anddetermine, based at least in part on the second set of data items, thepost-implant report.

Example 66

A system according to any of Examples 39 through 55, wherein the secondsubsession comprises a patient-status subsession, wherein to determinethe second set of data items, the one or more processors are configuredto: receiving user-input data in accordance with the second subsessionof the interactive session; and determining, from the user-input data,the second set of data items.

Example 67

A system according to Example 66, wherein the user-input data comprisesone or more of: patient-entered data, medication information, symptominformation, physiological metrics, or anatomical metrics.

Example 68

A system according to any of Examples 66 or 67, wherein the interactivesession comprises a third subsession and a fourth subsession, whereinthe third subsession comprises a physiological-parameter subsession andthe fourth subsession comprises a device-check subsession, wherein todetermine the abnormality, the one or more processors are configured to:determine, in accordance with the third subsession, a third set of dataitems, the third set of data items comprising the at least onephysiological parameter of the patient; determine, in accordance withthe fourth subsession, a fourth set of data items, the fourth set ofdata items comprising interrogation data; and determine the abnormalitybased at least in part on the third set of data items or the fourth setof data items.

Example 69

A system according to any of Examples 39 through 68, wherein to providethe second subsession, the one or more processors are configured to:initiate the second subsession subsequent to the first subsession.

Example 70

A system according to Example 69, wherein to determine the first set ofdata items, the one or more processors are configured to: receive, viacommunication circuitry of the computing device, the first set of dataitems, and wherein to determine the second set of data items, the one ormore processors are configured to: receive, via a UI of the computingdevice, the second set of data items.

Example 71

A system according to any of Examples 39 through 70, wherein the secondset of data items comprises information indicative of: an abnormality ofthe at least one physiological parameter of the patient or anabnormality of a device parameter corresponding to one or more medicaldevices.

Example 72

A system according to any of Examples 39 through 71, wherein todetermine indication of the abnormality, the one or more processors areconfigured to: output, to an abnormality determiner for an abnormalityanalysis, at least one of the first set of data items or the second setof data items; and determine a result of the abnormality analysis,wherein the result indicates the abnormality.

Example 73

A system according to Example 72, wherein the abnormality determinerincludes at least one of an AI engine or a ML model.

Example 74

A system according to any of Examples 72 or 73, wherein to determine theabnormality, the one or more processors are configured to: deploy theabnormality determiner to determine the abnormality, wherein theabnormality determiner is trained to identify, based at least in part onthe image data, the abnormality.

Example 75

A system according to any of Examples 39 through 74, wherein to outputthe post-implant report, the one or more processors are configured to:output the post-implant report to a device of one or more HCPs via acomputing network.

Example 76

A system according to any of Examples 39 through 75, wherein the user isthe patient.

Example 77

A system according to any of Examples 39 through 76, wherein the one ormore processors are configured to: determine that the interactivesession is complete; and provide a notification that the interactivesession is complete.

Example 78

A system according to Example 77, wherein to output the post-implantreport, the one or more processors are configured to: output a result ofthe post-implant report for display.

Example 79

A system according to any of Examples 39 through 78, wherein the systemcomprises a computing device, wherein the computing device comprises atleast one of the processors and at least one of the cameras, wherein theat least one processor is configured to: determine, based at least inpart on the first set of data items and the second set of data items,the abnormality.

In some implementations, the above-described Examples 1-38 and/or 39-79can be implemented using an apparatus comprising one or more means forperforming some or all of the various operations. As an Example 78, anapparatus for monitoring a patient of an IMD includes: means forproviding an interactive session to a user, the interactive sessionconfigured to allow the user to navigate a plurality of subsessionscomprising at least a first subsession and a second subsession distinctfrom the first subsession, wherein the first subsession comprisescapturing image data via one or more cameras; means for determining afirst set of data items in accordance with the first subsession of theinteractive session, the first set of data items including the imagedata; means for determining a second set of data items in accordancewith the second subsession of the interactive session, the second set ofdata items distinct from the first set of data items and comprising oneor more of: data obtained from an IMD, at least one physiologicalparameter of a patient, or user-input data; means for determining, basedat least in part on the first set of data items and the second set ofdata items, an abnormality corresponding to at least one of the patientor the IMD; and means for outputting a post-implant report of theinteractive session, wherein the post-implant report includes indicationof the abnormality and/or indication of an amount of time that hastranspired since the date of implantation of the IMD.

In some implementations, the above-described Examples 1-38 and/or 39-79can be implemented using a computer-readable storage medium storinginstructions that when executed cause one or more processors of a deviceto perform some or all of the various operations. As an Example 79, acomputer-readable storage medium can be provided storing instructionsthat, when executed, cause one or more processors of a system or devicefor monitoring a patient of an IMD to: provide an interactive session toa user, the interactive session configured to allow the user to navigatea plurality of subsessions comprising at least a first subsession and asecond subsession distinct from the first subsession, wherein the firstsubsession comprises capturing image data via one or more cameras;determine a first set of data items in accordance with the firstsubsession of the interactive session, the first set of data itemsincluding the image data; determine a second set of data items inaccordance with the second subsession of the interactive session, thesecond set of data items distinct from the first set of data items andcomprising one or more of: data obtained from an IMD, at least onephysiological parameter of a patient, or user-input data; determine,based at least in part on the first set of data items and the second setof data items, an abnormality corresponding to at least one of thepatient or the IMD; and output a post-implant report of the interactivesession, wherein the post-implant report includes indication of theabnormality and/or indication of an amount of time that has transpiredsince the date of implantation of the IMD.

Example 80

A method comprising: obtaining, by processing circuitry of a computingdevice, image data associated with a patient body; and determining, bythe computing device based on the obtained image data, a present statusof an implant-related wound on the patient body.

Example 81

A method according to Example 80, further comprising prompting a mobiledevice user to capture the image data using the mobile device apredetermined time after an implantation resulting in theimplant-related wound.

Example 82

A method according to any of Examples 80 or 81, obtaining, by processingcircuitry of a mobile device, data associated with functioning of amedical device implanted within a patient body; and determining, basedon the captured image data, performance metrics of the medical devicebased on the obtained data.

Various examples have been described. However, one skilled in the artwill appreciate that various modifications may be made to the describedexamples without departing from the scope of the claims. For example,although described primarily with reference to ECG parameters, in someexamples, other physiological parameters (e.g., impedance) or deviceparameters (e.g., temperature sensor data) may be used as evidence of apotential abnormality (e.g., device migration), where such evidence maybe used in conjunction with the image data to accurately determine thepresence of the potential abnormality, such as within a degree ofcertainty or at a particular confidence interval (e.g., greater than X %confidence, greater than 95% confidence, etc.).

It is to be recognized that depending on the example, certain acts orevents of any of the techniques described herein can be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,not all described acts or events are necessary for the practice of thetechniques). Moreover, in certain examples, acts or events may beperformed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors, rather than sequentially.

Based upon the above discussion and illustrations, it is recognized thatvarious modifications and changes may be made to the disclosedtechnology in a manner that does not necessarily require strictadherence to the examples and applications illustrated and describedherein. Such modifications do not depart from the true spirit and scopeof various aspects of the disclosure, including aspects set forth in theclaims.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium and executedby a hardware-based processing unit.

Computer-readable media may include computer-readable storage media,which corresponds to a tangible medium such as data storage media, orcommunication media including any medium that facilitates transfer of acomputer program from one place to another, e.g., according to acommunication protocol. In this manner, computer-readable mediagenerally may correspond to (1) tangible computer-readable storage mediawhich is non-transitory or (2) a communication medium such as a signalor carrier wave. Data storage media may be any available media that canbe accessed by one or more computers or one or more processors toretrieve instructions, code and/or data structures for implementation ofthe techniques described in this disclosure. A computer program productmay include a computer-readable medium.

By way of example, and not limitation, such computer-readable datastorage media can comprise RAM, ROM, EEPROM, CD-ROM or other opticaldisk storage, magnetic disk storage, or other magnetic storage devices,flash memory, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage media and data storage media do not includeconnections, carrier waves, signals, or other transitory media, but areinstead directed to non-transitory, tangible storage media. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Instructions may be executed by one or more processors, such as one ormore DSPs, general purpose microprocessors, ASICs, FPGAs, CPLDs, orother equivalent integrated or discrete logic circuitry. Accordingly,the term “processor,” as used herein may refer to any of the foregoingstructure or any other structure suitable for implementation of thetechniques described herein. Also, the techniques could be fullyimplemented in one or more circuits or logic elements.

Any of the above-mentioned “processors,” and/or devices incorporatingany of the above-mentioned processors or processing circuitry, may, insome instances, be referred to herein as, for example, “computers,”“computer devices,” “computing devices,” “hardware computing devices,”“hardware processors,” “processing units,” “processing circuitry,” etc.Computing devices of the above examples may generally (but notnecessarily) be controlled and/or coordinated by operating systemsoftware, such as Mac OS, iOS, Android, Chrome OS, Windows OS (e.g.,Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, WindowsServer, etc.), Windows CE, Unix, Linux, SunOS, Solaris, Blackberry OS,VxWorks, or other suitable operating systems. In some examples, thecomputing devices may be controlled by a proprietary operating system.Conventional operating systems control and schedule computer processesfor execution, perform memory management, provide file system,networking, I/O services, and provide UI functionality, such as GUIfunctionality, among other things.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including an integrated circuit (IC) or a setof ICs (e.g., a chip set). Various components, modules, or units aredescribed in this disclosure to emphasize functional aspects of devicesconfigured to perform the disclosed techniques, but do not necessarilyrequire realization by different hardware units.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method of monitoring a patient of animplantable medical device (IMB), the method comprising: providing, viaa computing device, an interactive session configured to allow a user tonavigate a plurality of subsessions comprising at least a firstsubsession and a second subsession distinct from the first subsession,wherein the first subsession comprises capturing image data via one ormore cameras; determining, via the computing device, a first set of dataitems in accordance with the first subsession of the interactivesession, the first set of data items including the image data;determining, via the computing device, a second set of data items inaccordance with the second subsession of the interactive session, thesecond set of data items distinct from the first set of data items andcomprising one or more of: data obtained from the IMD, at least onephysiological parameter of the patient, or user-input data; determining,based at least in part on the first set of data items and the second setof data items, an abnormality corresponding to at least one of thepatient or the IMD; and outputting, via the computing device, apost-implant report of the interactive session, wherein the post-implantreport includes indication of the abnormality and indication of anamount of time that has transpired since a date of implantation of theIMB.
 2. The method of claim 1, wherein determining the post-implantreport comprises: determining, from the second set of data items, theabnormality; and determining, based at least in part on the abnormalityand the first set of data items, the post-implant report.
 3. The methodof claim 1, wherein providing the interactive session comprises:training, via the computing device, a session generator on one or moreof: cohort parameters or IMD information; deploying, via the computingdevice, the session generator to generate the interactive session; andproviding, via the session generator, the interactive session that, as aresult of the training, is at least in part personalized for the user.4. The method of claim 1, wherein providing the interactive sessioncomprises: identifying a follow-up schedule for the patient, thefollow-up schedule defined by one or more time periods in which thecomputing device is configured to prompt the user to conduct theinteractive session; and providing the interactive session in accordancewith the follow-up schedule.
 5. The method of claim 4, whereinidentifying the follow-up schedule comprises: receiving, viacommunication circuitry of the computing device, a push notification;and determining, based on the push notification, a first time period forthe follow-up schedule.
 6. The method of claim 4, wherein the one ormore time periods include at least one time period that corresponds to apredetermined amount of time from the date of implantation of the IMD.7. The method of claim 1, wherein the image data comprises one or moreframes that represent images of a body of the patient.
 8. The method ofclaim 1, wherein determining the first set of data items comprises:providing, via the computing device, a prompt for the user to utilizethe one or more cameras to capture the image data; and determining,subsequent to the prompt, at least a portion of the first set of dataitems.
 9. The method of claim 1, wherein the second subsession comprisesa physiological-parameter subsession, wherein determining the second setof data items comprises: receiving, in accordance with the secondsubsession, at least one physiological parameter corresponding to thepatient; and determining, from the at least one physiological parameter,the second set of data items.
 10. The method of claim 9, wherein the atleast one physiological parameter comprises at least one of: anelectrocardiogram (ECG) parameter, a respiration parameter, an impedanceparameter, an activity parameter, or a pressure parameter.
 11. Themethod of claim 1, wherein the second subsession comprises adevice-check subsession, wherein determining the second set of dataitems comprises: performing, via communication circuitry of thecomputing device, an interrogation of one or more medical devicescorresponding to the patient, wherein the one or more medical devicesinclude the IMD; and determining, from the interrogation, the second setof data items.
 12. The method of claim 1, wherein the second subsessioncomprises a patient-status subsession, wherein determining the secondset of data items comprises: receiving user-input data in accordancewith the second subsession of the interactive session, wherein theuser-input data comprises one or more of: patient-entered data,medication information, symptom information, physiological metrics, oranatomical metrics; and determining, from the user-input data, thesecond set of data items.
 13. The method of claim 12, wherein theinteractive session comprises a third subsession and a fourthsubsession, wherein the third subsession comprises aphysiological-parameter subsession and the fourth subsession comprises adevice-check subsession, wherein determining the abnormality comprises:determining, in accordance with the third subsession, a third set ofdata items, the third set of data items comprising the at least onephysiological parameter of the patient; determining, in accordance withthe fourth subsession, a fourth set of data items, the fourth set ofdata items comprising interrogation data; and determining theabnormality based at least in part on the third set of data items or thefourth set of data items.
 14. The method of claim 1, wherein the user isthe patient, wherein the patient corresponds to one or more healthcareprofessionals (HCPs), and wherein outputting the post-implant reportcomprises: outputting, by the computing device, the post-implant reportto a device of the one or more HCPs via a computing network.
 15. Asystem for monitoring a patient of an implantable medical device (IMD),the system comprising: a memory configured to store image data; and oneor more processors in communication with the memory, the one or moreprocessors configured to: provide an interactive session configured toallow a user to navigate a plurality of subsessions comprising at leasta first subsession and a second subsession distinct from the firstsubsession, wherein the first subsession comprises capturing the imagedata via one or more cameras; determine a first set of data items inaccordance with the first subsession of the interactive session, thefirst set of data items including the image data; determine a second setof data items in accordance with the second subsession of theinteractive session, the second set of data items distinct from thefirst set of data items and comprising one or more of: data obtainedfrom the IMD, at least one physiological parameter of the patient, oruser-input data; determine, based at least in part on the first set ofdata items and the second set of data items, an abnormalitycorresponding to at least one of the patient or the IMD; and output apost-implant report of the interactive session, wherein the post-implantreport includes indication of the abnormality and indication of anamount of time that has transpired since a date of implantation of theIMD.
 16. The system of claim 15, wherein the second subsession comprisesa physiological-parameter subsession, wherein to determine the secondset of data items, the one or more processors are configured to:receive, in accordance with the second subsession, at least onephysiological parameter corresponding to the patient; and determine,from the at least one physiological parameter, the second set of dataitems.
 17. The system of claim 15, wherein the second subsessioncomprises a device-check subsession, wherein to determine the second setof data items, the one or more processors are configured to: perform aninterrogation of one or more medical devices corresponding to thepatient, wherein the one or more medical devices include the IMD; anddetermine, from the interrogation, the second set of data items.
 18. Thesystem of claim 15, wherein the interactive session comprises a thirdsubsession and a fourth subsession, wherein the third subsessioncomprises a physiological-parameter subsession and the fourth subsessioncomprises a device-check subsession, wherein to determine theabnormality, the one or more processors are configured to: determine, inaccordance with the second subsession, user-input data; determine, inaccordance with the third subsession, the at least one physiologicalparameter of the patient; determine, in accordance with the fourthsubsession, interrogation data; and determine the abnormality based atleast in part on the user-input data, the at least one physiologicalparameter, or the interrogation data.
 19. The system of claim 15,wherein the system comprises a computing device, wherein the computingdevice comprises at least one of the processors and at least one of thecameras, wherein the at least one processor is configured to: determinethe first set of data items by receiving, from the at least one camera,the image data; determine, based at least in part on the first set ofdata items and the second set of data items, the abnormality.
 20. Anon-transitory computer-readable storage medium having stored thereoninstructions that, when executed, cause one or more processors to:provide an interactive session to a user, the interactive sessionconfigured to allow the user to navigate a plurality of subsessionscomprising at least a first subsession and a second subsession distinctfrom the first subsession, wherein the first subsession comprisescapturing image data via one or more cameras; determine a first set ofdata items in accordance with the first subsession of the interactivesession, the first set of data items including the image data; determinea second set of data items in accordance with the second subsession ofthe interactive session, the second set of data items distinct from thefirst set of data items and comprising one or more of: data obtainedfrom an implantable medical device (IMD), at least one physiologicalparameter of a patient, or user-input data; determine, based at least inpart on the first set of data items and the second set of data items, anabnormality corresponding to at least one of the patient or the IMD; andoutput a post-implant report of the interactive session, wherein thepost-implant report includes indication of the abnormality.